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

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

ii 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 (NO x) 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 NO x 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 iii ultimate goal is to determine how the level of NO x 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 NO x over the engine’s operating domain. Several fuel consumption and NO x 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 NO x emission at the cost of fuel economy.

Results show that quite significant levels of NO x reduction can be had at a small cost in increased fuel use. However, even at reduced engine-out levels, NO x 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 NO x is also explored in this thesis.

iv 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.

v 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. NO x GENERATION AND CONTROL BY EXHAUST AFTERTREATMENT 16

2.1 Diesel Combustion and NO x Formation 16

2.2 Effects of Engine Operation on Efficiency and NO x Emission Level 18

2.3 Aftertreatment Methods for NO x Control 22

2.3.1 Lean NO x Traps 22

vi 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 NO x 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

vii 5.1.1 Fuel Use Mapping 74

5.1.2 Nitrogen Oxide Emission Mapping 77

5.1.3 Fuel Consumption and NO x 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

viii 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 NO x 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 NO x reduction via aftertreatment to meet USEPA standards 83

ix 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 NO x 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 lb m/hr 74

5.2 Engine mapping of brake specific fuel consumption, units in lb m/(hp*hr) 75

5.3 Engine mapping of fuel efficiency, units in percent 75

5.4 Volumetric NO x emission content as a function of bmep 77

5.5 Engine mapping of NO x mass flow rate, units in lb m/hr 78 x 5.6 Engine mapping of brake specific NO x emission, units in lb m/(hp*hr) 79

5.7 Engine mapping of NO x emission index, units in lb m/lb m 80

5.8 Error evaluation of response models 84 (a) fuel mass flow rate - lb m/hr (b) bsfc - lb m/(hp*hr) (c) fuel efficiency - % (d) NO x mass flow rate - lb m/hr (e) bsNO x - lb m/(hp*hr) (f) NO x emission index – lb m/lb m

xi 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 1 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.

2 Battery

Engine Generator Drive Motor

(a) series architecture

Engine Transmission

Battery Motor/Generator

(b) parallel architecture

Battery Generator

Drive Motor Engine Transmission

(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

3 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.

Motor/Generator Ultracapacitors

Transmission Engine Drive Motor

Front Drive Rear Drive

Figure 1.2: University of Akron ChallengeX hybrid vehicle architecture

4 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

5 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. 6 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

(NO x), carbon monoxide (CO), and un-burnt hydrocarbons (HC). Diesel exhaust emissions contain lower levels of CO and HC with NO x 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

7 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.

8 Table 1.1: USEPA legislation for tier 2 classified vehicles

Bin USEPA Allowable Emission Limits Per Bin - g/mi (lb m/mi)

# NMHC CO NO x 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. 9 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]:

[HC] + [O 2 ] → [CO 2 ] + [H 2O] (1.1)

[CO] + [O 2 ] → [CO 2 ] (1.2)

The resulting water (H2O) and carbon dioxide (CO 2) are not considered contributors to pollution by the USEPA, although CO 2 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

10 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

11 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 (NO 2) 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 NO 2 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

NO 2 levels required for regeneration are a problem in themselves and will be discussed in detail later.

12 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 (NO 2). Collectively these

13 compounds are commonly referred to as simply NO x. 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 NO x 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 (O 2) in the diesel exhaust stream will bond before promoting dissociation of NO x in the catalyst.

Implication that chemical conversion of NO x 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

NO x 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, NO x levels over a drive cycle will increase dramatically. A few modern technologies that may be feasible are starting to break the surface for diesel NO x control including lean traps that are capable of storing NO x 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 NO x 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.

14 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 NO x emission reduction without increased fuel consumption.

Diesel engine emission of nitrogen oxides will be explored in the remainder of this thesis. NO x generation and management via exhaust aftertreatment systems will be discussed. An efficient experiment design will be developed to evaluate both NO x 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. NO x 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 NO x emission to meet USEPA tier 2 standards. The results of lowered NO x emission will be weighed against the resulting decrease in fuel economy.

15 CHAPTER II

NO x 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 NO x 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 NO x traps and selective catalyst reduction for aftertreatment will be explained along with results from previous studies.

2.1 Diesel Combustion and NO x 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 C 12 H26 , but diesel hydrocarbon compounds range from C 10 H22 to C 15 H32 [15].

Complete combustion of hydrocarbons with excess oxygen results in the formation of only water and carbon dioxide as follows:

[C x H y ] + [O 2 ] → [ H 2O] + [CO 2 ] (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

16 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 NO x compounds. At combustion temperatures, molecular nitrogen (N 2) and oxygen (O 2) in the combustion air disassociate and bond with each other; a process commonly referred to as thermal NOx generation.

Most of the NO x generated in combustion is of the form nitric oxide (NO) and is governed by the following reactions.

O + N 2 → NO + N (2.2)

N + O 2 → NO + O (2.3)

N + OH → NO + H (2.4)

In much less quantities, but still a significant pollution source is the generation of nitrogen dioxide (NO 2), which is produced in the following manner from nitric oxide and oxygen.

2NO + O 2 → 2NO 2 (2.5)

The amount of NO x 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 NO x emission levels.

17 2.2 Effects of Engine Operation on Efficiency and NO x 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, m& f . 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, Pb , 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.

m& f bsfc= (2.6) Pb

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 18 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:

P n Workper cycle = b R (2.7) N

where Pb is engine power, nR 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, Vd , will yield bmep.

P n bmep = b R (2.8) Vd N

To simplify the computation, power can be expressed as the product of brake torque, Tb , 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, nR is 2. Subbing these values into equation (2.8) gives the form of equation (2.9); an example of proper unit conversion is given.

 rev  rad  2 2π ()lb *in  cycle  rev  4π *Tb    lb bmep = ⇒ ⇒ 2 (2.9) V  3  in d in cycle  

19 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.

Peak Torque Curve

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 20 effective in terms of reduced fuel usage. The question that remains is what happens to

NO x 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 NO x generation would be expected to be high in these areas as well due to higher temperatures. Effects of engine operation and NO x generation are well documented [16] and trends show that NO x levels increase with bmep. The following

Figure 2.2 shows a typical NO x/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 NO x 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 NO x emission as a function of bmep [16] 21 In hybrid vehicle operation, where obtainment of a high level of fuel economy is of the utmost concern, engine out NO x 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 NO x 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 NO x Control

It was mentioned in Chapter 1 that gasoline and diesel fueled engines exhibit similar NO x 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 NO x.

2.3.1 Lean NO x Traps

Until recently, mass produced diesel passenger vehicles have not contained any means for NO x control. New for the 2007 model year, Mercedes Benz was the first auto maker to introduce a model equipped with a lean NO x trap, a promising technology that is capable of NO x adsorption under lean (oxygen rich) conditions. A system as such operates by use of a catalyst that promotes adsorption and storage of NO x 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

22 regeneration, of course reducing fuel economy. Studies have shown that NO x 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 NO x 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 NO x compounds to form non- harmful ones. While several reducing agents have been studied with limited success, injection of ammonia (NH 3) 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 NO x compounds rather than be oxidized to form N 2, N 2O, and NO [17].

4NH 3 + 4NO + O 2 → 4N 2 + 6H 2O (2.10)

2NH 3 + NO + NO 2 → 2N 2 + 3H 2O (2.11)

8NH 3 + 6NO 2 → 7N 2 +12H 2O (2.12)

Greater than 90% of NO x from diesel emissions is composed of NO and thus reaction (2.10) accounts for most of the reduction as it occurs with NO and NH 3 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 NO 2. Reaction (2.12) takes care of the remaining NO 2 that cannot be reduced by (2.11) due to insufficient NO.

23 While ammonia can be an effective NO x 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 (CON 2H4) 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).

CON 2 H 4 → NH 3 + HNCO (2.13)

The HNCO compound is then further reduced by hydrolysis (reaction with water), which is plentifully available from the aqueous solution.

HNCO + H 2O → NH 3 + CO 2 (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 NO x 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.

24 Noble (precious) metal catalysts have proven highly active in NO x reduction, but also effectively oxidize NH 3 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 NO x 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

25 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 NO x 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

26 characteristics and thus calibration to each specific system is necessary. Mathematical modeling techniques utilizing chemical equations for urea decomposition and NO x 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

NO x 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 NO x 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 NO x reduction rates of 70-

85% depending on particular drive cycles. NO x 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 NO x conversion rates be even higher as it will always operate at high load, assuming the light-

27 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.

28 CHAPTER III

EXPERIMENT DESIGN AND SETUP

3.1 Experiment Overview

The focus of this research is to evaluate NO x 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 NO x 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 NO x

emission per distance traveled.

• Evaluate the effects of aiming the hybrid control strategy to operate the engine for

reduced NO x generation at the cost of fuel economy.

• Evaluate the level of NO x 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. 29 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 NO x 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 NO x 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 NO x 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

30 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 NO x 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

31 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 NO x 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 (NO x 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

32 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

33 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, yˆi for a given xi . 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:

2 ˆ 2 RSS = ∑ei = ∑(yi − yi ) (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.

2 RSS (y − yˆ ) s 2 = = ∑ i i (3.2) n − ()k +1 n − ()k +1

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 34 yˆ (i) be a regression estimator of the same type as yˆi except for the fact that the

regression is performed without the point (xi , yi ) , xi being the independent variable. Now

the residual for an observation yi and regression estimator yˆ (i) can be defined as follows:

eˆ(i) = yi − yˆ (i) (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:

n 2 ˆ 2  ˆ  PRESS = ∑e(i) = ∑  yi − y(i)  (3.4) i=1  

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 yˆ (i) 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

35 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 NO x 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] 36 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.

50 50

40 40

30 30

20 20 Power (hp) Power Torque (ft*lb) Torque

10 10

0 0 0 2000 4000 6000 8000 Speed (rpm)

Peak Torque Peak Continuous Torque Peak Power Peak Continuous Power

Figure 3.2: Siemens ACW-80-4 PM motor performance curves 37 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:

Pgen = Tengine N engine (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.

Pgen Tengine = (3.6) N engine

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.

38 200

160

120

80 Torque(ft*lb)

40

0 0 1000 2000 3000 4000 5000 Engine Speed (rpm)

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.

39 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 NO x 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.

η = β + β X + β X + β X 2 + β X 2 + β X X 0 1 1 2 2 11 1 22 2 12 1 2 (3.7) 3 3 2 2 + β111 X 1 + β 222 X 2 + β112 X 1 X 2 + β122 X 1 X 2

40 In the particular case of the research being presented, the equation response η will represent either fuel economy or NO x 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.

41 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).

160

140

120

100

80

Torque, ft*lb Torque, 60

40 Data Collection Points 20 Maximum Torque Profile

0 1000 1500 2000 2500 3000 3500 4000 4500 Engine Speed, rpm

Figure 3.4: Graphical representation of data collection points

42 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 NO x 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

43 response surface in Figure 3.5 shows the results of the statistical evaluation for the design in question over the relevant domain. Predicted Error Variance

Engine Load – lb*ft Engine Speed - RPM

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. 44 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.

Dynamometer Test Engine Flowmeters

Dyno Fuel Emissions Engine Controller Supply Sampling Diagnostics

Measured Data: Measured Data: Measured Data: Engine Speed/Load --- Communication Volume comp. of Mass Air Flow Fuel Use O , CO, NO, NO , HC Physical Flow 2 2

Figure 3.6: Test setup schematic

Dyno Controller

Engine

Flowmeters MAF Sensor

Exhaust Dynamometer Sampling Port

Figure 3.7: Test setup 45 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).

46 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

NO x 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 lb m/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 lb m/hr kg/hr lb m/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

47 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 NO x 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, m& fuel , per measured output power, Pb . 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,

m& fuel m& fuel bsfc= = (4.1) Pb Tb N

where Tb 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

48 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.

Measured Power P T N Fuel Efficiency = = b = b (4.2) Fuel Power Potential m& fuel HHV m& fuel HHV

Using the collected data for fuel usage and assuming the fuel as common diesel having HHV of 19,733 BTU/lb m, 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 lb m/hr lb m/(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

49 4.1.2 NO x Emission Analysis

Evaluation of NO x emission from the recorded data is more complex than the fuel usage analysis. It was mentioned in chapter 3 that laboratory evaluation of NO x 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 NO x 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, NO x 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 m& N ,a represent the mass flow rate of nitrogen from some gas component a .

m  = m + m + m  (4.3)  & N ,N2   & N ,N2 & N ,NO & N ,NO2   int  exh

The mass flow rate of nitrogen from molecular nitrogen (N 2) 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 NO 2 within the total dry exhaust volume are known and therefore their content can be expressed relative to one another. If the volumetric content of N 2 in a dry sample of the exhaust stream were known, its content could be expressed in relation to NO and NO 2 as well; facilitating the

50 computation of NO x mass flow rate from nitrogen-containing compounds of interest in the exhaust. Volumetric content data for exhausted N 2 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 (CO 2), water (H 2O), and the same amount of atmospheric nitrogen (N 2) 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 (C xHy) in air. Note the equation is not balanced; simply be aware of the reactants and products.

    C x H y + O 2 + N 2 → C x H y + O 2 + N 2 + CO 2 + H 2O + CO + NO + NO 2 (4.4)   int   exh

Examining the exhaust emission products, the anticipated N 2, CO 2, and H 2O are present.

In addition, some unburnt fuel (CxHy) as well as carbon monoxide (CO), nitrogen oxide

(NO), and nitrogen dioxide (NO 2) are the result of incomplete combustion and thermal

51 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 H 2O 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 N 2 composition of the exhaust stream which will facilitate NO x 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.

 1 1 1  n = n + n + n + n + n + n  (4.5a) O2 ,int  O2 CO2 2 H 2O 2 CO 2 NO NO2   ,exh

 1 1  n = n + n + n  (4.5b) N2 ,int  N2 2 NO 2 NO2   ,exh

 2  n = n + n  (4.5c) Cx H y ,int  Cx H y y H 2O   ,exh

 1 1  n = n + n + n  (4.5d) Cx H y ,int  Cx H y x CO2 x CO   ,exh 52 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 N 2 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:

reactants = n C H + n O + n N  (4.6)  fuel x y O2 2 N2 2   int

For the analysis of combustion per mole intake oxygen, equation (4.6) can simply be divided by the molar oxygen content.

 n n  reactants  fuel N2  = C H + O + N (4.7) n  n x y 2 n 2  O2  O2 O2   int

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.

n = n + n = .3 774n + n = .4 774n (4.8) air N2 O2 O2 O2 O2

n n fuel = .4 774 fuel (4.9) n n O2 air

53 Now the molar fuel/air ratio is calculated from the recorded fuel and air mass flow rates and the molar mass of each component.

n fuel m& fuel M fuel = (4.10) nair m& air M air

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.

  reactants m& fuel M fuel =  .4 774 C H + O + .3 774N  (4.11)  x y 2 2  nO m& air M air 2  int

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 O2 .

m& fuel M fuel n = .4 774 (4.12a) Cx H y m& air M air

n = 1 (4.12b) O2

n = .3 774 (4.12c) N2

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.

1  1 1  n − n = n + n + n + n + n  (4.13a) O2 ,int 2 H 2O,exh  O2 CO2 2 CO 2 NO NO2   ,exh

54  1 1  n = n + n + n  (4.13b) N2 ,int  N2 2 NO 2 NO2   ,exh

2 n − n = n (4.13c) Cx H y ,int y H 2O,exh Cx H y ,exh

 1 1  n = n + n + n  (4.13d) Cx H y ,int  Cx H y x CO2 x CO   ,exh

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.

n α a ≡ a (4.14) nb α b

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 n and application of equation (4.14) yields the O2 following:

1 n − n O2 ,int H 2O,exh 1  1 1  2 = α + α + α + α + α  (4.15a) n α  O2 CO2 2 CO 2 NO NO2  O2 ,exh O2  

n   N2 1 1 1 ,int = α + α + α  (4.15b) n α  N2 2 NO 2 NO2  O2 ,exh O2   55 2 n − n Cx H y ,int H 2O, exh α y Cx H y = (4.15c) n α O2 ,exh O2

n   Cx H y , 1 1 1 int = α + α + α  (4.15d) n α  Cx H y x CO2 x CO  O2 ,exh O2  

Unknowns in the above system of equations (4.15a-d) are the molar exhaust quantities of water and oxygen, n and n respectively, as well as the dry exhaust mole H 2O,exh O2 ,exh fractions of carbon dioxide and nitrogen, α and α respectively. The mole fraction CO2 N2 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:

1 1 1 n − n α + α + α + α + α O2 ,int H2O,exh O2 CO2 CO NO NO2 2 = 2 2 (4.16a) n 1 1 N2 ,int α + α + α N2 2 NO 2 NO2

2 n − n Cx H y , H 2O,exh int y α C H = x y (4.16b) n 1 1 Cx H y ,int α + α + α Cx H y x CO2 x CO

Now solving both equations (4.16a,b) for the exhausted water content, n , the H 2O,exh following equations are formed:

 1 1   α + α + α + α + α  O2 CO2 CO NO NO2 n = 2n − n 2 2  (4.17a) H 2O,exh  O2 ,int N2 ,int 1 1   α + α + α   N2 NO NO2   2 2 

56     y α C H n = n 1− x y  (4.17b) H 2O,exh 2 Cx H y ,int  1 1   α + α + α   Cx H y CO2 CO   x x 

The system of two equations (4.17a,b) still contains three unknown parameters, n , H 2O,exh

α and α . Recognition that the mole fraction values for dry exhaust ( α terms) must CO2 N2 equal unity allows solution of the system.

α = α + α + α + α + α + α + α = 1 (4.18) ∑ exh,dry Cx H y O2 N2 CO2 CO NO NO2

Equations (4.17a,b) and (4.18) could now be combined to solve the parameter of interest,

α . However, the resulting equation form would be rather complex and difficult to work N2 with. Instead, equation (4.18) is solved for α . N2

α = 1−α −α −α −α −α −α (4.19) N2 Cx H y O2 CO2 CO NO NO2

Now a value for α can be guessed and a corresponding α calculated via equation CO2 N2

(4.19). The α and α can then be used to solve equations (4.17a,b) for n and CO2 N2 H2O,exh 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 NO x 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.

m  = m + m + m  (4.20)  & N ,N2   & N ,N2 & N ,NO & N ,NO2   int  exh 57 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.

 M n  =  M n + M n + M n  (4.21)  N2 & N2   N2 & N2 N & NO N & NO2   int  exh

Because only mole fractions of exhausted N 2, NO, and NO 2 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.

n = γ n (4.22) &a a & N2

Applying equation (4.22) to equation (4.21) and also noting that the molar mass of N is half the molar mass of N 2, equation (4.21) can be rewritten in the following manner.

1 1  M n  = γ  M n  + γ M n  + γ  M n  (4.23)  N2 & N2  N2  N2 & N2  NO  N2 & N2  NO2  N2 & N2   int  int 2  int 2  int

Recognizing that the product of M and n yields the mass flow rate of nitrogen, N2 & N2 equation (4.23) is rewritten on an intake nitrogen mass flow rate basis.

1 1 m  = γ m  + γ m  + γ m  (4.24)  & N ,N2  N2  & N ,N2  NO  & N ,N2  NO2  & N ,N2   int  int 2  int 2  int

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. 58 m  = γ m  (4.25a)  & N ,N2  N2  & N ,N2    exh  int

1 m  = γ m  (4.25b) & N ,NO NO  & N ,N2   exh 2  int

1 m  = γ m  (4.25c)  & N ,NO2  NO2  & N ,N2   exh 2  int

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 γ a . In order to apply

exhaust mole fraction data, γ a must be related to the exhaust data.

Reverting back to the nitrogen mass flow balance equation (4.24), further

simplification allowing examination of γ a values as exhaust content parameters is possible.

1 1 1 = γ + γ + γ (4.26) N2 2 NO 2 NO2

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 :

α γ = a (4.27) a 1 1 α + α + α N2 2 NO 2 NO2 where a represents N 2, NO, or NO 2 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 59 nitrogen mass flow rate of the intake air. However, note that the mass flow rates of NO and NO 2 as a whole are of interest, not simply the nitrogen component of the compounds.

The mass flow rate of either NO or NO 2 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 NO 2 are as follows.

M NO   m& NO,exh = m& N ,NO  (4.28a) M N  exh

M m = NO2 m  (4.28b) & NO2 ,exh  & N ,NO2  M N  exh

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.

m n = & air = n + n (4.29) &air & N2 &O2 M air

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 N 2/O 2. This assumption can be directly applied to equation (4.29).

n .4 774n & N2 & N2 n& N + n&O = n& N + = (4.30) 2 2 2 3.774 3.774

60 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.

.3 774 m n = & air (4.31) & N2 .4 774 M air

The mass flow rate of nitrogen from intake air can now be solved by multiplication of the molar mass of N 2. Equation (4.31) becomes the following.

.3 774M m  = m = N2 m (4.32)  & N ,N2  & N2 ,int & air,int  int .4 774M air

The presented equations for NO x evaluation to this point can now be combined to yield two equations that will facilitate calculation of the mass flow rates of NO and NO 2.

Specifically, beginning with equations (4.28a,b) for exhausted NO x 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 γ a parameters.

1 M m = NO γ m  (4.33a) & NO,exh NO  & N ,N2  2 M N  int

1 M NO m = 2 γ m  (4.33b) & NO2 ,exh NO2  & N ,N2  2 M N  int

Now equation (4.27) is applied to the γ a 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 NO 2 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 N 2 is twice the molar mass of N, the following simplified expressions are quite easy to work with.

61 .3 774M NO α NO m& NO,exh = m& air,int (4.34a) .4 774M 1 1 air α + α + α N2 2 NO 2 NO2

.3 774M α NO2 NO2 m& NO ,exh = m& air,int (4.34b) 2 .4 774M 1 1 air α + α + α N2 2 NO 2 NO2

The global exhaust volumetric concentrations (mole fractions) α and α as well as NO NO2

the intake mass air flow rate m& air,int are contained within the collected data. The volumetric content of exhausted nitrogen, α , must still be solved for by iteration to N2 satisfy equations (4.17a,b) and (4.19). Solving of α is reliant on all of the measured N2 dry exhaust concentrations including C xHy, O 2, CO, NO, and NO 2 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 NO x is of course the sum of the mass flow rates of exhausted NO and NO 2.

m = m + m (4.35) & NOx ,exh & NO,exh & NO2 ,exh

In terms of engine mapping, nitrogen oxide mass flow rate can be presented as simply the NO x 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 NO x 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 NO x emission ( bs NO x) is simply the NO x mass flow rate divided by the engine’s measured brake power.

62 m& NO ,exh m& NO ,exh bsNOx = x = x (4.36) Pb Tb N

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 C 12 H26 , 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 NO x mass flow rate and bs NO x.

Table 4.3: Calculated values for NO x emission

Eng. Speed Eng. Torque Mass Flow Rates ( lb m/hr ) bs NOx

rpm lb*ft NO NO 2 NO x 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 NO x 63 emission to fuel use by mass is presented in this thesis for baseline engine mapping purposes. The quantity is calculated by simply dividing the NO x mass flow rate by the fuel mass flow rate.

m& NO ,exh EI = x (4.37) NOx m& fuel

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, NO x mass flow rate, brake specific NO x emission, and NO x emission index:

64 2  1 dy  e =  U  (4.38) y ∑ y dx xi   i 

where y represents an equation governing the computation of a parameter of interest, xi is a variable with uncertainty, and U is half the precision of the measurement device. xi

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 m * bs NO ** EI ** & NOx x NOx 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 m . Higher relative & NOx uncertainties displayed for bs NO and EI are a result of the NO mass flow rate, x NOx x relatively small in magnitude, only being able to be computed accurately to one significant digit. Calculated bs NO and EI were only used for baseline engine x NOx

65 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

X 1 and X 2 with regression coefficients βi .

η = β + β X + β X + β X 2 + β X 2 + β X X 0 1 1 2 2 11 1 22 2 12 1 2 (4.39) 3 3 2 2 + β111 X 1 + β 222 X 2 + β112 X 1 X 2 + β122 X 1 X 2 + ε

In the particular cases of interest for this research, the output variable η is representative of fuel use parameters or NO x emission output in any one of the forms presented

previously in this chapter. The control variables X 1 and X 2 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 βi 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

66 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 NO x 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, Tb , has units of lb*ft.

η = β + β N + β T + β N 2 + β T 2 + β NT 0 1 2 b 11 22 b 12 b (4.40) 3 3 2 2 + β111 N + β 222Tb + β112 N Tb + β122 NTb

The summary of the collection of βi 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 ( m& fuel ), brake specific fuel consumption (bsfc), fuel efficiency (eff), NO emission mass flow rate ( m ), brake x & NOx specific NO emission ( bsNO ), and NO emission index ( EI ). Equation (4.40) can x x x NOx

be used by direct application of the βi coefficients. However, input of N and Tb 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 βi 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.

67 Table 4.5: Regression results summary η : m m EI & fuel bsfc eff & NOx bsNO x NOx

Units: lb m/hr lb m/(hp*hr) % lb m/hr lb m/(hp*hr) lb m/lb m

β 0 18.0573 0.33433 38.3700 0.9152 0.015370 0.04850

β1 11.0545 0.01997 -2.4402 0.5451 0.003321

β 2 7.2055 -0.02579 2.6234 0.5095 0.003758 0.01427

β11 1.6193 0.01915 -1.7731 -0.1967 -0.00923

β 22 1.1238 0.03527 -3.0548 -0.1301 -0.002924 -0.01077

β12 4.0680 -0.00970 0.9258 0.0504 -0.002469

β111 -0.2142 -0.004149

β 222

β112 0.8746 0.01410 -1.6231 -0.3643 -0.005931 -0.02065

β122 0.5631 0.01414 Domain mapping of independent variables for regression equation use: 2 2 3 3 2 2 η = β 0 + β1 N + β 2Tb + β11 N + β 22Tb + β12 NTb + β111 N + β 222Tb + β112 N Tb + β122 NTb Engine Speed - N (rpm): [1250:4000]
[-1:1]

Engine Torque - Tb (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

NO x emission index provide good baseline models for the tested engine, the regression models that simply describe mass fuel use and mass NO x 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

68 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 NO x 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.

69 60

40

20

Vehicle Velocity - miles/hour 0 0 200 400 600 800 1000 1200 1400 Time - seconds 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 NO x 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.

70 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 NO x mass flow rate as functions of engine speed (rpm) and load (lb*ft) to determine instantaneous fuel and NO x flow rates at a given simulation data point. Let these parameters determined from regression models be referred to as m  N 3 ,T 3  and m  N 3 ,T 3  respectively for a & fuel  b  & NOx  b   i  i given time step i . Once these values have been determined for each time step in the drive cycle, fuel economy and NO x 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, δt , for n total time steps in the drive cycle.

n n  3 3   3 3  fuel use = ∑ m& fuel  N ,Tb  δti = δt∑ m& fuel  N ,Tb  (4.41) i=1  i i=1  i

The average combined fuel economy over the drive cycle is obtained by division of the

total distance traveled, d cycle , by the fuel use as follows. Multiplication by the fuel’s

density, ρ fuel , allows fuel economy to be expressed in terms of distance traveled per volume fuel consumed (mpg).

d cycle d cycle FE = ρ fuel = ρ fuel (4.42) fuel use  3 3  δt∑ m& fuel  N ,Tb   i

71 Like fuel use, the total mass of NO x emission can be calculated over the drive cycle. A sum of the product of instantaneous mass NO x emission rate and the taken time step, δt , for n total time steps in the drive cycle provides this value.

n n NO emission = m  N 3 ,T 3  δt = δt m  N 3 ,T 3  (4.43) x ∑ & NOx b i ∑ & NOx b i=1  i i=1  i

Average NO x emission is determined by division of the total NOx emission over the drive

cycle total distance, d cycle .

n  3 3  δt m& NO  N ,Tb  NO emission ∑ x   m = x = i=1 i (4.44) & NOx d cycle dcycle

The computations for drive cycle analysis of fuel economy and NO x 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 NO x at the cost of increased fuel usage. The results of which are detailed in Chapter 5.

72 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 NO x 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 NO x emission and determine what level of NO x 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 NO x 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

73 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.

2 9 150 . 3 3 39.13 23 2 2 6 3 5 37. 2 6 8 2 .6 .0 3 2 5 . . 3 5 6 7 . 9 2 9 .4 73 1 1 2 5 0 6 8 6 15 4 6 .8 4 6 4 4 9 8 140 .6 .0 3 7 1 6 4 7 3 2 0 7 9 8 1 7 9 4 .4 32. 7 30.981 2 3 2 4 130 3 4 .1 8 5 1 1 9 0 1 2 .6 .9 1 120 0 6 3 1 4 3 .3 7 1 5 2 .3 8 1 9 .7 5 6 5 9 5 2 3 9 110 2 2 .6 0 26 8 2 .1 2 . .2 3 1 2 5 9 6 3 2 .5 0 4 8 . 4 6 6 9 8 7 1 100 . 1 1 3 7 2 4 6 2 0 4 . . 1 7 6 6 0 . 0 7 3 4

Engine Torque -Torque lb*ft Engine 8 7 7 9 4 2 90 9 3 24 7 .1 .8 8 8 9 7 2 6 1 1 80 1 0 1 . . 1 1 1 6 9 3 7 8 0 6 . .3 . 6 4 3 3 9 7 . 5 2 5 5 1 6 5 2 5 9 3 2 70 9 0 1 .1 4 1 38 60 1500 2000 2500 3000 3500 4000 Engine Speed - RPM fuel use - lb /hr m Peak Eng. Trq. Cont. Pwr. Gen. Max. Pwr. Gen.

Figure 5.1: Engine mapping of fuel mass flow rate, units in lb m/hr

74 0 0 0 0 6 0 .3 .3 0 . .3 150 4 .3 46 5 . 3 8 42 38 5 0 35 7 0 3 .3 3 0. 3 6 4 5 . 1 0 9 0.3 9 3 0 3 7 3 34 4 67 2 7 38 32 24 9 .3 6 0 1 140 0 .3 0. 7 5 33 0 0 2 0 . 30 2 3 9 2 .3 5 6 5 3 0 6 4 8 130 3 8 .3 0

120

1 4 8 7 2 110 8 6 9 5 3 2 3 . 3 3 .3 4 8 5 0 025 33 33 6 6 0 0.33 0. 0. 4 50 46 .3 3 67 42 0 0. 4 100 .3 35 0.3 0 0. 3432

Engine Torque- lb*ft Engine 5 09 88 37 7 90 0.33839 66 0. 31 0.3 .38 24 2 0 7 246 53 50 .38 0.34 .346 6 4 1 .37 1 0 31 0 .350 3587 628 0 379 91 80 0 467 0. 0.3 0. 0.3 0.35 45 9 8 095 399 52 0756 70 668 .37 538 0. 4030.4116 0.3 0 317 .39 0. 0.4 5738 2 .38 4 0 .41419 74 1 50 0 872 00. 58 28 0.37 91 0.3 387 0.3 .36 0.37 .42 60 0 0 1500 2000 2500 3000 3500 4000 Engine Speed - RPM bsfc - lb /(hp*hr) m Peak Eng. Trq. Cont. Pw r. Gen. Max. Pwr. Gen.

Figure 5.2: Engine mapping of brake specific fuel consumption, units in lb m/(hp*hr)

6 3 3 150 68 8 2 . 3 4 . 6 5 1 . 7 4 20 2 3 5 2 3 9 1 0 . .6 8. 64 5 3 6 1 8 3 4 7 . 37 .5 4 3 7 38 . 1 1 140 6 3 9 9 6 5

3 3 3 4 8 . 8 9 8 6 7 . 3 9 9 . .

5 2 6 7 0

4 6 . 8 2 8 130 4 4 6 8 2 4 4 6 97 2 8. 7 3 3 9 120 4 1 7 6 . 5 . 4

5 3

3 110 05 12 724 8. 1 100 38.9 3 7 64 46 8 8.54 .69 68 9 .2 3 37 1 13 4 Engine Torque- lb*ft Engine 86 .4 . 3 .26 7 36 08 35 90 37 842 .99 36. 35 .1205 52 38 49 12 43 93 .56 13 .86 3. 0 80 946 35 4.7 33 3 3.0 37.6 3 3 86 34 .26 168 .58 37 27 36.4 1 32 6.84 908 .139 .287 70 3 35.9 35 34 649 574 15 5.5 13 612 352 2.1 .73 055 96 7 3 34.7 33.8 33.4 3 31 31.3 0.87 0.453 60 3 3 1500 2000 2500 3000 3500 4000 Engine Speed - RPM fuel efficiency - % Peak Eng. Trq. Cont. Pw r. Gen. Max. Pwr. Gen.

Figure 5.3: Engine mapping of fuel efficiency, units in percent

75 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

76 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 NO x content of dry exhaust is plotted vs. brake mean effective pressure in figure 5.4.

1800 Measured Data 1600 Linear (Measured

1400

1200

1000

800

600

400

200

Volumetric NOx content of dry exhaust - ppmv - exhaust dry of content NOx Volumetric volumetric NOx = 8.4353*bmep - 117.77

0 0 50 100 150 200 250 Brake mean effective pressure - PSI

Figure 5.4: Volumetric NO x emission content as a function of bmep 77 The plot and linear regression model displayed in figure 5.4 can prove useful for modeling and simulating NO x emission. For a desired engine load, bmep can be calculated and volumetric NO x emission estimated from the linear regression equation. If means for temperature and exhaust flow rate prediction at a common point are also available, mass NO x 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 NO x 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 NO x mass flow rate and brake specific NO x emission. 1.1464 0 1

. . 1 150 5 2 7 1 .3 0 8 8 . 7 0 1 5 . 79 8 0 . 0 8 8 4

5 6 . 2 6 3 6 0 6 1 9 9 3 4 4 0 9 5 . . 1 7 . 3 0 140 1 0 5 7 . 8 2 7 2 0 2 3 9 5 1 7 . 4 2 6 1 1 5 0 6 0 . .9 130 7 3 1 2 4 7 1 0 1 8 8 .2 1 1 120 .0 04 8 1. 110 1464 1.0 0 756 0 .8 100 .5 63 0 8 0 0 3 .4 0. 0 .6 .7 2 38 50 29 5 9 0. Engine Torque - lb*ft Engine 1 2 9 0.3 7 9 0 5 34 1.004 68 7 53 4 6 08 8 90 0 0. 1 72 18

80 0. 297 0 25 .86332 0.226 0 49 0 0. .79 70 0 .4 58 0 25 .3 38 0.5 02 .65 6 0 68 77 09 9 10 .1557 01 53 4 0.7 3 218 60 1500 2000 2500 3000 3500 4000 Engine Speed - RPM mass NO out - lb /hr x m Peak Eng. Trq. Cont. Pwr. Gen. Max. Pwr. Gen.

Figure 5.5: Engine mapping of NO x mass flow rate, units in lb m/hr 78 0 0 8 . 150 6 0 0 0 . 9 6 0 0 1 3 0 1 . . 1 7 2 3 0 0 . 0 4 2 .0 1 0 1 0 6 6 . 1 1 5 1 0 2 1 5 .0 1 8 1 1 4 0 1 1 1 9 0 0 7 2 6 0 . 7 . 3 0 140 . 0 2 0 1 6 9 6 0 9 1 5 3 0 3 5 .0 1 7 1 1 4 130 2 0 0 0 . .0 0 3 1 1 0 5 . 7 6 0 120 52 2 0 1 6 .0 5 8 1 2 5 3 6 7 2 5 110 5 0.0 2 7 0 6 14 .0 0 5 3 71 15 .0 1 1 2 9 2 6 0 5 3 2 . 6 6 1 0 8 0 .0 100 0 9 1 . 0 4 1 .0 20 0 7 13 3 3 68 4 0 1 7 0 Engine Torque- lb*ft Engine . 1 2 17 0 0 3 1 90 . 4 01 5 0 1 0. 5 75 .0 65 2 0 2 0 7 1 3 0 .0 8 .0 62 0 .0 13 0 6 0 9 11 .0 1 . 80 3 0.0 1 26 17 0 3 1 6 21 5 1 14 1 2 10 39 5 7 0 0 1 1 1 .0 . 0 01 9 1 0 . 0. 0.0 5 0 2 70 9 0.0 0. 0 0 1 5 10 01 .0 .0 42 3 0 0. 07 0 11 11 6 1 0.0 00 4 59 1 6 0 .0 0.0 090 95 0 2 3 0 085257 418 58 6 3 60 0.0080096 1500 2000 2500 3000 3500 4000 Engine Speed - RPM bsNO - lb /(hp*hr) Peak Eng. Trq. Cont. Pw r. Gen. Max. Pwr. Gen. x m Figure 5.6: Engine mapping of brake specific NO x emission, units in lb m/(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 NO x. 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 NO x alone is of course a good predictor of its emission. Examination of figure 5.5 yields that mass NO x 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

NO x emission. Figure 5.5 also suggests that, within the continuous power generation region, NO x 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

79 generation, increasing the time that the engine runs and in turn providing more time for

NO x emission.

Evaluation of figure 5.6 for brake specific NO x emission simply validates the assumption that regions of operation that yield low fuel consumption also yield high NO x 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 NO x 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 NO x emission index for the tested engine over the relevant domain.

0 . 150 0 0 0 2 8 5 . . 0 0 0 8 9 1 7 4 . 3 4 2 9 0 3 3 3 7 4 3 3 7 1 7 0 4 2 0 8 140 . 0 1 8 3 . 5 . 0 0 2 0 2 4 8 0 . 3 7 4 4 0 5 5 2 5 4 4 7 3 1 4 8 3 0 0 2

0 3 . . 8 6 0 4 . 0 5 130 6 . 0 0 0 9 7 9 3 6 5 0 . 4 3 4 0 8 7 7 5 5 1 0 0 1 5 6 . 5 . 3 6 0 2 5 0 0 . 1 7 0 8 4 1 5 . 0 0 120 0 . 3 0 1 9 4 6 4 1 5 2 110 0.049328 4 0 . 0 0.047384 100 0 0.0 .043 45441 498 0.043498 Engine Torque -Torque lb*ft Engine 90 0.041555 0.041555 0.039612 0.039612 80 0.037668 8 0.035725 0.0 766 37 .03 0.0337 66 0 5 82 0.0 8 72 3782 0.031839 35 70 35 0.03 72 .0 9 0.029896 5 0 83 0. 031 52 0.0279 029 0. .0279 0.026009 52 896 60 0 0.024066 1500 2000 2500 3000 3500 4000 Engine Speed - RPM

EI-NO - lb /lb Peak Eng. Trq. Cont. Pwr. Gen. Max. Pw r. Gen. x m m Figure 5.7: Engine mapping of NO x emission index, units in lb m/lb m 80 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

NO x 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 NO x 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 NO x 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 NO x formation at this point should still produce a lowered value and the engine will run as little as possible, fuel efficiency is again slightly

81 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 NO x 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 NO x emission. Average values were computed for fuel economy and engine-out NO x 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 NO x emission reduction for the two scenarios aimed at lowered NO x 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. NO x Fuel Economy NO x Engine Operation Economy Emission Reduction Reduction

Speed, RPM Load, lb*ft mi/gal lb m/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 NO x reduction via aftertreatment required to meet USEPA Tier 2 requirements per bin are given in table 5.2.

82 Table 5.2: Required NO x reduction via aftertreatment to meet USEPA standards Target Series Mode Required NO x 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 NO x 0.20 0.15 0.10 0.07 0.04 0.03 0.02 0.00 emission per bin - g/mi

The results presented in table 5.1 show that quite desirable results can be achieved in engine-out NO x 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 NO x generation at a cost of just under 5% fuel economy.

However, note in examining table 5.2 that even at 25% NO x 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 NO x 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

83 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.

40 0.46

35 0.44

30 0.42

25 0.4

20 0.38

Predicted 15 Predicted 0.36

10 0.34

5 0.32

0 0.3 0 5 10 15 20 25 30 35 40 0.32 0.34 0.36 0.38 0.4 0.42 0.44 0.46 Observed Observed (a) fuel mass flow rate - lb m/hr (b) bsfc - lb m/(hp*hr)

1.4 42

40 1.2

38 1

36 0.8

34 0.6 Predicted Predicted 32 0.4 30 0.2 28 0 26 0 0.2 0.4 0.6 0.8 1 1.2 1.4 28 30 32 34 36 38 40 42 Observed Observed (c) fuel efficiency - % (d) NO x mass flow rate - lb m/hr 0.07 0.02

0.06

0.05 0.015

0.04 Predicted

Predicted 0.03 0.01

0.02

0.01 0.005 0.02 0.025 0.03 0.035 0.04 0.045 0.05 0.055 0.008 0.01 0.012 0.014 0.016 0.018 0.02 Observed Observed (f) NO x emission index – lb m/lb m (e) bsNO x - lb m/(hp*hr) Figure 5.8: Error evaluation of response models

84 The presented error evaluation plots illustrate the accuracy of the models for fuel mass flow rate, bsfc, fuel efficincy, and NO x mass flow rate to be quite good predictors of the actual response. Evaluations of bsNO x and the NO x emission index are somewhat questionable. However, realize that evaluation of bsNO x was simply to provide standardized evaluation of NO x emission relative to fuel use by comparison to bsfc. Also, the NO x 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 NO x, appear to be excellent predictors.

85 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 NO x 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 NO x at a small sacrifice in fuel economy. However, even with the observed 25% reduction of engine-out NO x, comparison to USEPA standards shows a large NO x 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 NO x 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 NO x 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

86 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 NO x emission without sacrifice of DPF regeneration, some suggestions for future research in this area can be made. The target

NO x 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 NO x 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 NO x 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 NO x reduction via aftertreatment can also be had by addition of a lean

NO x trap downstream of the SCR system, capable of storing a percentage of the NO x 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

87 downstream of the SCR, the NO x 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 NO x 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 NO x could be had intermittently while still allowing periodic DPF regeneration. Another option to reduce engine-out NO x is alteration of the engine’s injection timing. Retarding the start of injection results in lower combustion temperatures and thus lower thermal NO x 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 NO x 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.

88 REFERENCES

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89 12. A. Hinz, L. Andersson, J. Edvardsson, P. Salomonsson, C. J. Karlsson, F. 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 Diesel Engine”, SAE International 2005-01-1084.

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17. P. Forzatti, “Present status and perspectives in de-NO x SCR catalysis”, Applied Catalysis A: General 222 (2001) 221-236.

18. H. Lunders, R. Backes, G. Huthwohl, D. A. Ketcher, R. W. Horrocks, R. G. Hurley, and R. H. Hammerli, “An Urea NO x Catalyst System for Light Duty Diesel Vehicles”, SAE International 952493.

19. C. Lambert, B. Hammerle, R. McGill, M. Khair, and C. Sharp, “Technical Advantages of Urea SCR for Light-Duty and Heavy-Duty Diesel Applications”, SAE International 2004-01-1292.

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90 24. M. Block, N. Clark, S. Wayne, R. Nine, and W. Miller, “An Investigation into the Emissions Reduction Performance of and SCR System Over Two Years’ In-Use Heavy-Duty Vehicle Operation”, SAE International 2005-01-1861.

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91

APPENDICES

92 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 X ,1 i and X ,2 i represent designation of two independent variables for n total data points.

 2 2 3 3 2 2  1 X 1,1 X 1,2 X 1,1 X 1,2 X 1,1 X 1,2 X 1,1 X 1,2 X 1,1 X 1,2 X 1,1 X 1,2  1 X X X 2 X 2 X X X 3 X 3 X 2 X X X 2  X =  2,1 2,2 2,1 2,2 1,1 2,2 2,1 2,2 2,1 2,2 2,1 2,2  (A.1) ......  1 X X X 2 X 2 X X X 3 X 3 X 2 X X X 2   ,1 n ,2 n ,1 n ,2 n ,1 n ,2 n ,1 n ,2 n ,1 n ,2 n ,1 n ,2 n 

If the actual model was known, the β regression coefficients would be known and the observations would be presented by the following matrix equation:

η = Xβ + ε (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.

−1 var()ε = (X T X ) MSE (A.3)

93 Because the actual model can not be known, only predicted coefficients can be determined. They can be computed using the observed experimental data.

−1 βˆ = (X T X ) X Tη (A.4)

Variance for the prediction model can be calculated in the same manner as the actual model could if it were known.

−1 var(βˆ)= (X T X ) MSE (A.5)

Now consider a point in the domain defined by independent variables X ,1 p and

X ,2 p that the regression model can predict. Define the regression matrix for this point of interest as follows.

 2 2 3 3 2 2  x = 1 X 1,p X 2,p X 1,p X 2,p X 1,p X 2,p X 1,p X 2,p X 1,p X 2,p X 1,p X 2,p  (A.6)  

The prediction model for this point of interest is found in the following manner.

−1 ηˆ = xβˆ = x(X T X ) X Tη (A.7)

Calculation of variance at the predicted point from the regression model yields the predicted error variance for that point in the domain.

−1 −1 PEV()()x = var ηˆ = (x(X T X ) X T )(X (X T X ) xT )MSE (A.8)

The PEV calculation can be simplified as follows.

−1 PEV()x = x(X T X ) xT MSE (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. 94 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  1 dy  e =  U  (A.10) y ∑ y dx xi   i 

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 NO x 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

m& fuel N 1 rpm bsfc = P b Tb 1 lb-ft

P Pb 1 hp* efficiency = b m HHV & fuel m& fuel 0.01 lb m/hr

m& NO m& air,int 0.1 lb m/hr** bsNOx = x P m 0.1 lb /hr b & NOx m

m& NO * Computed from N and T b EI = x NOx m& fuel ** Computed to 1 sig. fig.

95 Applying the equations and resolutions listed in table A.1, the following uncertainty evaluations are obtained.

2 2     P .005 5. m& fuel e = b   +   (A.11) bsfc m  P   P 2  & fuel  b   b 

2 2     m& fuel  5.   .005P  e = + b (A.12) eff    2  Pb  m& fuel HHV   m fuel HHV     & 

2 2     P .05 5. m& NO e = b   +  x  (A.13) bsNOx    2  m& NO Pb Pb x    

2 2 m    .005m  & fuel  .05   & NOx  e = + (A.14) EI _ NOx    2  m& NO  m& fuel   m& fuel  x    

Evaluation of uncertainty for the rate of NO x 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 NO x can be written as follows.

.3 774 mair,int   m& NO = M NOα NO + M NO α NO  (A.15) x .4 774M 1 1  2 2  air α + α + α N2 2 NO 2 NO2

The value for α was a calculated value itself derived from iteration of multiple N2 equations for which relative uncertainty cannot be computed. The variable having the least measured resolution used in the computation of α was accurate to one significant N2

96 digit, thus the calculated values for α are used in error analysis to a precision of 0.1. N2

To further simplify the computation, recognize that mole fractions α and α were NO NO2 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.

2 2      .05 dm& NO   .05 dm& NO  e = x + x (A.16) m& NOx      m& NO dm& air,int   m& NO dα N   x   x 2 

Evaluation and simplification leads to the following form which is able to be applied.

1 2  2   2         .05   .05   em =  +  (A.17) & NOx  m   1 1   & air,int α + α + α      N2 NO NO2    2 2    

97 APPENDIX C

DRIVE CYCLE SIMULATION RESULTS

Vehicle Speed - MPH 100 50 0 0 200 400 600 800 1000 1200 1400 Engine Speed - RPM 2000 1000 0 0 200 400 600 800 1000 1200 1400 Enging Torque - lb*ft 200 100 0 0 200 400 600 800 1000 1200 1400 Mass Fuel Use - lb /hr m 20 10 0 0 200 400 600 800 1000 1200 1400 Mass NO Emission - lb /hr x m 1.5 1 0.5 0 0 200 400 600 800 1000 1200 1400 Elapsed Time - seconds

Figure A.1: Simulation results targeting 1900 rpm, 78 lb*ft

98

Vehicle Speed - MPH 100 50 0 0 200 400 600 800 1000 1200 1400 Engine Speed - RPM 2000 1000 0 0 200 400 600 800 1000 1200 1400 Enging Torque - lb*ft 200 100 0 0 200 400 600 800 1000 1200 1400 Mass Fuel Use - lb /hr m 20 10 0 0 200 400 600 800 1000 1200 1400 Mass NO Emission - lb /hr x m 1.5 1 0.5 0 0 200 400 600 800 1000 1200 1400 Elapsed Time - seconds

Figure A.2: Simulation results targeting 1900 rpm, 65 lb*ft

99

Vehicle Speed - MPH 100 50 0 0 200 400 600 800 1000 1200 1400 Engine Speed - RPM 2000 1000 0 0 200 400 600 800 1000 1200 1400 Enging Torque - lb*ft 200 100 0 0 200 400 600 800 1000 1200 1400 Mass Fuel Use - lb /hr m 40 20 0 0 200 400 600 800 1000 1200 1400 Mass NO Emission - lb /hr x m 1.5 1 0.5 0 0 200 400 600 800 1000 1200 1400 Elapsed Time - seconds

Figure A.3: Simulation results targeting 2250 rpm, 65 lb*ft

100 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 lb m/hr kg/hr lb m/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

101 Table A.3: Calculated values for fuel consumption Eng. Speed Eng. Torque. Fuel Flow bsfc Fuel Efficiency

rpm lb*ft lb m/hr lb m/(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 NO x emission Eng. Eng. Speed Torque Alpha_N 2 Mass Flow Rates ( lb m/hr ) bs NOx

rpm lb*ft vol. NO NO 2 NO x 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

102 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 lb m/hr kg/hr lb m/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

Tb 60 lb*ft

m& air 304.2 lb m/hr

m& fuel 5.09 lb m/hr α 0.115 O2

α CO 0.000243

α NO 0.000554 α 0.000033 NO2 α 0.001105 Cx H y

Fuel use parameters are calculated as follows: (after proper unit conversion)

m& fuel .5 09 lbm / hr lbm bsfc = = = .0 356 Tb N 60 lb ft *1250 rpm hp * hr

T N 60 lb ft *1250 rpm Fuel Efficiency = b = = 36.17 % m& fuel HHV .5 09 lbm / hr *19733BTU / lbm

103 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.

 1 1   α + α + α + α + α  O2 CO2 CO NO NO2 n = 2n − n 2 2  H 2O,exh  O2 ,int N2 ,int 1 1   α + α + α   N2 2 NO 2 NO2        y α C H n = n 1− x y  H 2O,exh 2 Cx H y ,int  1 1   α + α + α   Cx H y x CO2 x CO    α = 1−α −α −α −α −α −α N2 Cx H y O2 CO2 CO NO NO2

Substituting for the molar intake parameters yields the following for an analysis per mole oxygen of the engine’s intake air:

 1 1   α + α + α + α + α  O2 CO2 CO NO NO2 n = 21− .3 774 2 2  H 2O,exh  1 1   α + α + α   N2 NO NO2   2 2      α 26 m& fuel M fuel Cx H y n = .4 774 1−  H 2O,exh 2 m M  1 1  & air air  α + α + α   Cx H y CO2 CO   12 12  α = 1−α −α −α −α −α −α N2 Cx H y O2 CO2 CO NO NO2

Now the known data can be substituted. It will be shown for this particular case a value of α = .0 802 and α = .0 081 satisfies the iterative solution. N2 CO2

 1 1   .0 115 + .0 081+ .0 000243 + .0 000554 + .0 000033    n = 2 1− .3 774 2 2 = .0 151967 H 2O,exh  1 1   .0 802 + .0 000554 + .0 000033     2 2      26 .2 31 170.33  .0 001105  n = .4 774 1− = .0 151900 H 2O,exh 2 138 28.97  1 1   .0 001105 + .0 081+ .0 000243     12 12  α = 1− .0 001105 − .0 115 − .0 081− .0 000243 − .0 000554 − .0 000033 = .0 802065 ≈ .0 802 N2 104 Now that the volumetric concentration of N 2 is known, the mass flow rates of nitrogen oxides in the exhaust stream are calculated as follows:

.3 774M NO α NO m& NO,exh = m& air,int .4 774M 1 1 air α + α + α N2 NO NO2 2 2 .3 774 * 30.01 .0 000554 = 304 2. lbm / hr  = .0 170 lbm / hr .4 774 * 28.07 1 1   .0 802 + .0 000554 + .0 000033 2 2 .3 774M α NO2 NO2 m& NO,exh = m& air,int .4 774M air 1 1 α N + α NO + α NO 2 2 2 2 .3 774*46.01 .0 000033 = 304 2. lbm / hr  = .0 0156 lbm / hr .4 774*28.07 1 1   .0 802 + .0 000554 + .0 000033 2 2

m = m + m = .0 170 + .0 0156 = .0 186 lbm / hr & NOx & NO & NO2

The NO x emission index is calculated from the fuel and NO x mass flow rates:

m& NO ,exh .0 186 EI = x = = .0 0365 NOx m& fuel .5 09

105