Model-Based Investigation of Lean Gasoline PM and Nox Control

Model-Based Investigation of Lean Gasoline PM and NOx Control

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Shreyas Shivaprasad

Graduate Program in Mechanical Engineering

The Ohio State University

2014

Master's Examination Committee:

Dr. Shawn Midlam-Mohler

Professor Yann Guezennec

Shreyas Shivaprasad

2014

Abstract

A flexible model-based vehicle platform was developed with all the appropriate aftertreatment components and sensors for a GDI engine having the ability to perform diagnostic analysis. This model was developed by integrating aftertreatment and sensor models available in the public domain to develop a platform capable of running different drive cycles and scaling across different vehicle platforms with minor changes. Faults were identified for each system and the model was added with the capability to inject those faults to analyze the ability of the sensor set to diagnose those faults. Results have been presented representing the working of the model when the faults are injected. A Design of

Experiments (DOE) study was conducted to explore the design space of the GPF and understand the impact of the design parameters on the performance of the filter. It was concluded from the study that smaller filters can be used due to low soot loading and soot can be regenerated passively without any need for external heating. It was observed that ash loading in the filter may be a critical issue during the long run. In addition, the scalable model was provided to an industrial research consortium as a tool for future precompetitive research.

Acknowledgements

I would like to thank Center for Automotive Research for providing me with an excellent environment to work towards my Master’s degree. I would like to extend my gratitude to my advisor, Dr. Shawn Midlam-Mohler, for giving me the opportunity to be a part of such a detailed and demanding endeavor and believing in my abilities for the entire duration of the work. I also owe a debt of gratitude to my parents, friends for the emotional and mental support who all on a daily basis unwaveringly listened to never-ending stories about the progress of the work. All in all, I had a very rewarding experience as a graduate student.

CAR is a special place which nurtured my individuality and constantly kept me motivated to explore new avenues.

iii

Dedication

Dedicated to loving parents and Ananya

Vita

2008 – 2012 ...... B.Tech. Production Engineering, . NIT, Trichy 2012 - 2014 ...... Graduate Research Associate, Center for Automotive Research, The Ohio State University

Fields of Study

Major Field: Mechanical Engineering

Table of Contents

Abstract ...... ii

Acknowledgements ...... iii

Vita ...... v

Table of Contents ...... vi

Table of Figures ...... ix

Chapter 1 Introduction ...... 1

Section 1.1 Scope of Work ...... 2

Section 1.2 Document Layout ...... 3

Chapter 2 Background and State of Art ...... 5

Section 2.1 Gasoline Engine Technologies ...... 5

2.1.1 Variable Valve Timing ...... 5

2.1.3 Exhaust Gas Recirculation...... 6

2.1.4 Injection Strategy Modification ...... 7

2.1.5 Gasoline Direct Injection ...... 8

Section 2.2 Emission Control Devices – An overview ...... 11

2.2.1 Three Way Catalytic Convertors ...... 11 vi

2.2.2 Lean NOx Trap ...... 13

2.2.3 Selective Catalytic Reduction (SCR) ...... 16

2.2.4 Particulate Filters ...... 18

Section 2.3 Engine Emissions Modelling ...... 21

Section 2.4 On Board Diagnosis of Emission Control Devices ...... 22

2.4.1 OBD in Vehicles – Brief History and State of Art ...... 22

2.4.2 On Board Diagnostic Regulations (Federal and California) ...... 25

2.4.3 Faults Diagnostic Methods in Emission Systems ...... 30

2.4.4 Sensors for Lean Burn Engines ...... 33

Chapter 3 Model Development of a Flexible Vehicle Platform ...... 37

Section 3.1 Introduction ...... 37

Section 3.2 Driver Model ...... 38

Section 3.3 Powertrain Model ...... 39

Section 3.4 Vehicle Model ...... 40

Section 3.5 Aftertreatment Model ...... 41

3.5.1 Engine Emissions Model ...... 43

3.5.2 Aftertreatment Components ...... 49

3.5.3 Modelling and Calibration of AFR controller for LNT Regeneration ...... 62

3.5.4 Model Application - Fault Modelling Approach ...... 63

Chapter 4 Design of experiments for GPF and Results of Fault Injection ...... 67

vii

Section 4.1 Model analysis – GPF design of experiments ...... 67

4.1.1 Change in Cell density/Wall thickness ...... 68

4.1.2 Change in Length of the Filter ...... 69

4.1.3 Change in Trap Diameter ...... 72

4.1.4 Change in Porosity...... 74

4.1.5 Passive Regeneration ...... 75

Section 4.2 Sample Model Results ...... 76

4.2.1 Engine Actuator Fault ...... 79

4.2.2 Multiplicative Sensor Fault in a AFR Sensor ...... 80

Chapter 5 Conclusions and Future work ...... 82

Section 5.1 Conclusions ...... 82

Section 5.2 Future Work ...... 85

References ...... 86

viii

Table of Figures

Figure 1 : Dependence of Concentration of Emission Species on Valve Timing ...... 6

Figure 2: Dependence of Injection Timing on PM Emissions ...... 7

Figure 3: Effect of AFR on Operation of TWC ...... 9

Figure 4: Particulate Number Emissions of Different Engines ...... 10

Figure 5: Particulate Mass Emissions (g/mile) from 9 Different Vehicles With GDI Engines

...... 10

Figure 6: Impact of Catalyst Conversion Efficiency Due to Change in Exhaust Gas

Temperature ...... 12

Figure 7: Phases Of Operation of Lean NOx Trap ...... 14

Figure 8: LNT Efficiency as a Function of Sulfur Concentration ...... 15

Figure 9: Impact of NOx Conversion Efficiency with Change in Exhaust Temperatures 15

Figure 10: Schematic Explaining the Working Of Urea/SCR ...... 16

Figure 11: NOx Conversion Efficiency as a Function of Ammonia Slip Ratio ...... 18

Figure 12 : Working of the Particulate filter ...... 19

Figure 13: Exhaust Temperatures for Lean Burn and Stoichiometric Engines at Different

Locations Along the Exhaust Pipe ...... 20

Figure 14 : Comparison of Predicted and Measured CO And NOx Emission ...... 21

Figure 15: Comparison of US and EU OBD Regulations for PM diagnosis ...... 30

Figure 16: Schematic of Model-Based Fault Diagnosis Process ...... 31

Figure 17: Resistive PM Sensor ...... 33

Figure 18: Phases of Soot Sensor Operation ...... 34

Figure 19: Three Stages During Working Of Capacitance Based Soot Sensor ...... 35

Figure 20: Quasi-Static vehicle simulator...... 37

Figure 21 : Schematic of the Aftertreatment Model ...... 41

Figure 22 : Snapshot of GT-POWER Model ...... 45

Figure 23: Engine-out CO Emissions from Two Vehicles with DI and PFI Engines on a

FTP-75 drive cycle ...... 46

Figure 24: Engine-out NOx Emissions from Two Vehicles with DI and PFI Engines on a

FTP-75 Drive Cycle ...... 46

Figure 25: Engine-Out and Tailpipe Emission Results before Scaling ...... 47

Figure 26: Scaling Factors and Emission Results after Scaling ...... 48

Figure 27: Schematic of LNT Modelling Methodology ...... 51

Figure 28: Design of a Particulate Filter ...... 56

Figure 29: Schematic of the Deep Bed Filtration Method ...... 57

Figure 30: Schematic of GPF Modelling Methodology ...... 59

Figure 31: Schematic of TWC Modelling Methodology ...... 61

Figure 32: AFR Controller Model ...... 62

Figure 33: Schematic of Fault Modelling Approach ...... 64

Figure 34: Impact of Trapping Efficiency with Change in Wall Thickness ...... 69

Figure 35: Average Pressure Drop over a FTP-75 Drive Cycle Simulation as a Function of

Time for Different Lengths of Filter ...... 71

Figure 36: Cumulative Tailpipe Soot Emissions over a FTP-75 Drive Cycle as a Function of Time for Different Lengths of Filter...... 71

Figure 37: Average Pressure Drop over a FTP-75 Drive Cycle Simulation as a Function of

Time for Different Diameters of Filter ...... 73

Figure 38: Cumulative Tailpipe Soot Emissions over a FTP-75 Drive Cycle as a Function of Time for Different Diameters of Filter ...... 73

Figure 39: Trapping Efficiency as a Function of Porosity of the Filter ...... 75

Figure 40: Soot Mass Retained in the Filter and Filter Substrate Temperature Over a .... 76

Figure 41 : Cumulative NOx Emission on a FTP-75 Drive Cycle ...... 77

Figure 42: Cumulative NOx conversion efficiency of this system on a FTP-75 Drive Cycle

...... 77

Figure 43: NOx fill ratio during a FTP-75 Drive Cycle ...... 77

Figure 44: NOx Sensor Output ...... 78

Figure 45: AFR sensor Output ...... 78

Figure 46: Pre-GPF Soot Sensor Output ...... 78

Figure 47: Engine out NOx Emissions With and Without Fault ...... 79

Figure 48: NOx Fill Ratio With and Without Fault ...... 79

Figure 49: Tailpipe-out NOx Emissions With and Without Fault ...... 80

Figure 50: AFR Sensor Output With and Without Fault ...... 80

Figure 51: Comparison of Model Results Vs Tailpipe Emissions from the Model...... 84

Figure 52: Overall Conversion Efficiency of All Species for the Considered System ..... 85

Chapter 1 Introduction

Emission regulations have been introduced by the government of United States to regulate the concentration of emission species such as HC, CO, NOx, etc. emitted into the environment. Various technologies have been developed from time to time to ensure regulation of the emission species according to the standards during that time. These regulations have been revised regularly to maintain a cleaner environment despite the fact that number of cars on the road are growing. In addition to the fact that we have to maintain a cleaner environment, dependence on non-renewable sources of energy is growing with increase in number of cars. Thus these regulations are also accompanied with fuel economy standards that the OEM have to meet to get the car in the market. There have been extensive research to improve fuel economy of an IC engine to meet these standards. The widely accepted solution among the OEMs to meet the new Corporate Average Fuel Economy

(CAFE) standards for gasoline engines is direct injection technology. It has been proven to increase the efficiency of engine operation by in stoichiometric operation which is the dominant GDI engine at present. Further gains can be achieved by running lean air fuel mixtures resulting in further decreases of CO2 emissions. Tier 3 emission standards expects the NMOG + NOx emissions, PM emissions from a gasoline to be regulated to 0.5 g/mile and 3 mg/mile respectively with the use of appropriate devices. With the use of direct

injection technology, the particulate and NOx emission concentrations are comparable to that from a diesel engine and thus may require the use of filters/traps to ensure conformance with the emission standards. It is likely that in-cylinder controls will be acceptable for certain applications, however, the concept of a gasoline particular filter is presently being discussed in the technical community.

Section 1.1 Scope of Work

This research project was funded by Center for Automotive Research (CAR) Industrial

Consortium to facilitate the creation of a flexible model-based vehicle platform with all the appropriate aftertreatment components and sensors for a GDI engine and having ability to perform diagnostic analysis. It integrates aftertreatment and sensor models available in the public domain to develop a model capable of running different drive cycles and scaling across different vehicle platforms with minor changes. The work serves as an example to show the possibility of developing an emission concentration prediction model using a combination of phenomenological and empirical models already in the public domain. A preliminary FMEA was conducted to identify different faults in each system that caused higher tailpipe emissions. Based on the modelling scheme, it was identified that only certain faults could be simulated using these models. Fault injection capability was added to the model by modelling three kinds of faults such as actuator faults, system faults and sensor faults and representative results have been shown in the later sections. A Design of

Experiments (DOE) study was designed to explore the design space of gasoline particulate filter and its impact on the pressure drop characteristics, trapping efficiency and the passive

regeneration capability. This work can be extended to develop a diagnostic algorithm using the sensor outputs in the model and can serve as a preliminary step in the diagnostic design for a vehicle.

Section 1.2 Document Layout

Following the introduction, this document contains four different chapters that sequentially describe the work.

Chapter 2: State of Art

This chapter describes the gasoline engine technologies and the after treatment devices available currently that reduce tailpipe emissions to meet the emission regulations. This chapter transitions into describing a background about On Board Diagnostic (OBD) systems, diagnostic regulations and fault diagnostic methods used for the after treatment devices and quoting results from literature.

Chapter 3: Model Development of flexible vehicle platform

This chapter describes the model in detail and the development process involved with each sub model such as driver, powertrain, vehicle, aftertreatment model and the process of integration to create a flexible model capable of scaling across various vehicle platforms.

It also describes the different faults considered and process of adding fault injection capability to the model.

Chapter 4: Model analysis and representative results

This chapter describes the results of Design of Experiments (DOE) study conducted using this model to study the impact of change in filters design parameters on the trapping efficiency, pressure drop characteristics and the passive regeneration capability.

Representative results that show the capability of the aftertreatment model to perform diagnostic analysis have been described in detail.

Chapter 5: Summary and future work

This chapter summarizes the different features of the model and describes the important conclusions from the Design of Experiments (DOE) study exploring the design space of the particulate filter. As this work was conducted with limited resources, a list of suggestions have been included to refine the model, improve its capability and perform diagnostic analysis to analyze the capability of a chosen sensor set for a specific platform.

Chapter 2 Background and State of Art

Section 2.1 Gasoline Engine Technologies

Modern gasoline engines have been known to be more fuel efficient and environmentally friendly. This advancement is due to the development of better engine management systems using sophisticated electronics and electro-hydraulic systems. A brief introduction will be provided about such technologies that have been developed in the recent past to reduce the engine out exhaust emissions. Some of the technologies are

- Variable Valve timing

- Gasoline direct injection

- Exhaust gas recirculation

- Injection strategy modification

2.1.1 Variable Valve Timing

Variable valve timing is a technique to alter the timing of valve lift event dynamically using the necessary actuators. This technique results in various outcomes such as improved performance to increase in fuel economy or reduced emissions. It can be achieved using mechanical or electrohydraulic or camless systems. Different manufacturers use the similar technology in their cars under different brand names such as

VTVT (Hyundai), VVT-I (Toyota-Lexus), VANOS (BMW), and VTEC (Honda). The 5

stricter fuel economy regulations are the primary motivation for the automakers to implement this technology in their cars. The Figure 1 shown below illustrates the effect of valve timing on the concentration of exhaust gas species which can also be an advantage of the technology.

Figure 1 : Dependence of Concentration of Emission Species on Valve Timing [1]

2.1.3 Exhaust Gas Recirculation

Exhaust Gas Recirculation (EGR) is a commonly used technique to reduce NOx emissions from a gasoline/diesel engine by recirculating the exhaust gas back into the combustion chamber to reduce the temperature of combustion. As the inert exhaust gas is recycled, it displaces some of the combustible mixture in the combustion chamber. The inert gas recycle reduces the combustion temperatures significantly. The NOx is formed

primarily when the nitrogen and oxygen are subjected high temperatures. As the combustion temperatures are lowered, the NOx emissions are reduced significantly.

2.1.4 Injection Strategy Modification

It was observed that modifying the strategy of fuel injection to create multiple pulses resulted in a more homogenous air fuel mixture that caused reduction in particulate emissions. In addition to this, a short pulse delivering a small quantity of fuel before ignition also helps in keeping the particulate emissions at acceptable levels [1].

Figure 2: Dependence of Injection Timing on PM Emissions [1]

2.1.5 Gasoline Direct Injection

The two different types of lean spark-ignited engines that are commonly available are PFI or GDI based. Lean PFI engines are those similar to stoichiometric port fuel injected engines with the same fuel injectors but with a modified combustion chamber geometry to facilitate burning of leaner air fuel mixtures. The combustion chamber geometry is such that the air-to-fuel ratio around the spark plug is capable of ignition, despite an overall lean mixture. Lean burn engines can handle air fuel ratios of approximately up to 25:1. This engine is capable of operating in either lean or stoichiometric with proper calibrations.

Direct-injection spark-ignited (DISI) gasoline engines different in a way that they rely on a high pressure fuel injector that injects fuel directly into the combustion chamber during the compression stroke, which is then ignited with a spark. This paves way for combusting leaner mixtures with air fuel ratios of up to 40:1. Though this technology offers improved efficiency of operation, NOx and PM emissions increases significantly during certain modes of operation. GDI engine improves efficiency by stratified operation and using high compression ratios. Use of high compression ratios also results in increased combustion temperatures. NOx emission is maximum at high cylinder temperatures and at around stoichiometric AFR. As torque output rises, temperatures rise and, in turn, the engine-out NOx emissions display an increase. As the NOx formation is primarily dependent on temperatures inside the combustion chamber, its concentration increases.

Soot formation is primarily promoted due to high fuel pressures and locally rich zones

during stratified operation. It is observed that GDI technology serve to reduce CO2 emissions significantly.

Figure 3: Effect of AFR on Operation of TWC

Three way catalytic convertors are commonly used to reduce NOx, CO and HC emissions in spark ignited engines. This device is efficient in a very narrow range of air fuel ratios as shown in the Figure 3. As seen from the figure below, NOx emissions increases due to reduced conversion efficiency with the use of lean air-fuel mixtures. The emission concentrations of GDI engine thus become comparable to that of diesel engines and requires the use of additional after treatment devices and better engine calibration methods to reduce the concentration of harmful engine out exhaust gas species.

Figure 4: Particulate Number Emissions of Different Engines [1]

Figure 5: Particulate Mass Emissions (g/mile) from 9 Different Vehicles With GDI Engines [2]

Test results shown in Figure 5 indicate that PM emissions for first generation GDI vehicles tested are significantly higher than PFI vehicles certified to California Low Emission 10

Vehicle (LEV) II standards. In phase 1 of the FTP cycle during engine cold-start, PM mass emissions for the nine GDI vehicles ranged from 4 to 35 mg/mi, with an average of 14 mg/mi [1].

Section 2.2 Emission Control Devices – An overview

The discussion so far has been focused on emissions from GDI engines and various calibration techniques that can be employed to reduce emissions. Further reductions in emissions can be achieved by the use of external devices that makes the exhaust gases undergo a chemical reaction to convert them to harmless gases. There are various types of after-treatment devices suitable for different applications such as catalytic convertors

(oxidizing catalysts for HC, CO and reducing catalyst for NOx and three way catalytic convertor for all three pollutants), Lean NOx traps for reducing NOx during lean operation of engine, particulate filters for reducing soot emissions and so on. These after-treatment devices will be discussed in detail below.

2.2.1 Three Way Catalytic Convertors

If the spark ignited engines are normally operated with air fuel ratios near stoichiometric, three way catalytic convertors can be used to reduce all three pollutants at the same time. Referring to the Figure 5 tells us that the conversion efficiency of this device is maximum only when the engine operates within a narrow window of air fuel ratios around stoichiometric. The width of this window is about 0.1 air/fuel ratios. The catalyst effectively converts HC, CO and NOx into H20, CO2 and N2 under these exhaust conditions as enough reducing gases will be present to reduce NOx and enough O2 will be present to 11

oxidize HC and CO. As the air fuel ratios need to be maintained within the narrow band to achieve high conversion efficiency, sophisticated systems with the use of electronics that enable closed loop control is needed. Oxygen sensor installed in the exhaust acts as a feedback device that provides information to the engine management system whether the engine is burning lean , stoichiometric or rich air-fuel mixtures. The noble metals such as platinum and rhodium are used as catalysts in the commercial production of convertors.

The variation of conversion efficiency of catalytic convertors vary with temperature as shown in the Figure 6 below. These devices are ineffective until its temperature has risen above 250-300ºC. These are the temperatures at which conversion efficiency of the catalytic convertor is more than 50 percent and are termed light off temperatures.

Figure 6: Impact of Catalyst Conversion Efficiency Due to Change in Exhaust Gas Temperature [3]

Degradation of these devices occur due to various reasons, such as deactivation of catalyst sites due to poisoning and reduction in effective area of these sites through sintering. Poisoning affects both warmup and steady state performance of the catalyst. It occurs due to the presence of lead in the fuel anti-knock agents, phosphorous in the oil additives Sintering occurs due to the prolonged exposure of the catalyst to high temperatures. It causes migration and agglomeration of active sites and thus reducing the effective surface area. It has a prominent effect on the warm-up of the catalyst than on the steady state performance.

2.2.2 Lean NOx Trap

The engines are operated with lean air fuel mixtures to improve the efficiency of engine operation. The downside of using lean air fuel mixtures is NOx emission become difficult to control because of selectivity issues with the excess oxygen. As discussed above, the TWC becomes less effective with the use of lean air fuel mixtures which means the conversion efficiency drops. Lean NOx traps is one of the effective solutions to reduce

NOx emissions from lean burn engines.

Lean NOx traps consist of an oxidation catalyst such as Pt, reduction catalyst such as Rh and a NOx adsorber such as barium, strontium or phosphorous oxides. During the lean engine operation, the trap adsorbs the NO2 by reacting with the metal oxides and converting into nitrates till the trap becomes saturated. To regenerate the trap, the engine is operated with rich mixtures to produce the reducing agents such as CO and HC so that the released NOx is converted into N2 and CO and HC are oxidized to CO2 and H2O. To

optimize the conversion efficiency of all the pollutants, proper control systems have to be developed which would essentially switch between rich and lean mixtures as and when required. The lean NOx trap requires the use of an oxidation catalyst upstream to convert the NO into NO2.

Figure 7: Phases Of Operation of Lean NOx Trap[4]

The conversion efficiency of the trap depends on various factors, such as catalyst concentration, active sites on the adsorber and trap temperature. Durability of NOx traps under sulfur contamination has always been a major problem. Sulfur poisoning of the trap due to sulfur content in the fuel and lubricant reduces the active sites on which NO2 could be adsorbed forming nitrates. Sulfur is removed by passing rich and hot steam of exhaust gases for about ten minutes every 5000-10000 Km .The Figure 8 below shows the effect of fuel with different quantities of sulfur on trap’s conversion efficiency.

Figure 8: LNT Efficiency as a Function of Sulfur Concentration [5]

The trap temperature also plays a major role in maintaining the conversion efficiency. It can be seen from the Figure 9 below that the conversion efficiency of the trap is at its maximum only between 250-450°C.

Figure 9: Impact of NOx Conversion Efficiency with Change in Exhaust Temperature [6]

2.2.3 Selective Catalytic Reduction (SCR)

Selective Catalytic Reduction (SCR) is one of the cost effective and fuel efficient emission control technologies that reduce NOx emissions by injecting urea, a liquid reactant through a catalyst into the stream of exhaust gases. The liquid reactant is urea, which is otherwise known as Diesel Exhaust Fluid (DEF). This solution sets off a chemical reaction which converts NO2 to N2 and H2O which is expelled out of the exhaust. This technology is termed selective because of the fact that ammonia from the urea solution chooses to react only with NOx and reduces it.

Figure 10: Schematic Explaining the Working Of Urea/SCR [7]

The disadvantage about using the Urea-SCR in the light duty or heavy duty automobile is the need for human intervention to keep the conversion efficiency high. This

is because the diesel exhaust fluid has to be replenished periodically to maintain its performance. For light duty automobiles, DEF refill intervals occur around the time of recommended oil change. For heavy duty automobiles, it depends on the hours of operation, no of miles driven, load factors, etc. Audio and visual indications are provided to the driver around the time when it needs to be refilled. Some manufacturers have a system in which when DEF levels are very low, starter mechanism in the car is disabled until replenished to prevent higher exhaust emissions. A nationwide DEF distribution infrastructure has expanded to meet the needs of the growing market.

SCR requires precise control of ammonia injection rate. Improper control may result in reduced conversion efficiencies. An injection rate which is too high will result in release of undesirable amount of ammonia into the atmosphere which is termed ammonia slip. This occurs when the ratio of ammonia and NOx is high. The Figure 11 below shows the variation of NOx conversion efficiency, percentage of ammonia slip with temperature and NH3/NOx ratio. It can be observed that the percentage of slip decreases with temperature but conversion efficiency depends on lot other factors such as particular temperature range and catalyst system, etc.

Figure 11: NOx Conversion Efficiency as a Function of Ammonia Slip Ratio [8]

2.2.4 Particulate Filters

Particulate filters act like traps that filters the soot particles in the exhaust gas stream.

Ceramic particulate filters consist essentially of a honeycomb structure which is made of either cordierite or silicon carbide which has large number of parallel channels. Channel sizes are specified by cell density, measured in cpsi (cells per square inch). Wall thickness is measured in micrometers. Typical filter wall thickness is around 300-400 µm and cell densities of around 100-300 cpsi.

Figure 12 : Working of the Particulate filter [9]

Particulate filters have to be periodically regenerated to reduce the back pressure on the engine due to higher soot loading and increase its trapping efficiency. The filter is regenerated by burning off the soot collected in the filter. The soot mass can be oxidized using the oxygen constantly present in the exhaust gas above a temperature of 600°C to form CO2. During the normal operation of diesel engine, exhaust gases do not reach such temperatures for it to burn off, measures have to be taken lower the soot burn off temperature or increase the exhaust gas temperature. This is achieved by two methods such as external heating to increase the exhaust gas temperatures or introducing metal additives in the fuel that reduce the soot burn off temperatures. Regeneration achieved through external heating is termed active regeneration and regeneration achieved by increasing the exhaust gas temperatures using in cylinder techniques is termed passive regeneration. The catalyzed particulate filters is also considered a solution to improve the soot burn off temperatures. These devices also perform additional function of reducing NOx and oxidizing CO and HC emissions. 19

Particulate filters have been a very established solution in the industry to reduce soot emissions from the diesel engines. With the widespread use of gasoline direct injection engines, there is a need for controlling particulate matter emissions from them. Thus, the particulate filters have been used with GDI engines to reduce the soot emissions. Exhaust temperatures in gasoline engines reach temperatures close to 500-600°C which makes it easier for soot burn off without the use of intrusive means often needed for Diesel particular filter management.

Figure 13: Exhaust Temperatures for Lean Burn and Stoichiometric Engines at Different Locations Along the Exhaust Pipe [10]

The Figure 14 above shows a comparison of exhaust gas temperatures from engines running stoichiometric and lean air fuel mixtures in NEDC driving schedule. It also compares and contrasts two methods of regeneration such as regeneration using oxygen in 20

the exhaust or using NO2 as a soot oxidizing agent. It can be observed from the above figure that the temperature required for NO2 based regeneration is much lower when compared to oxygen based regeneration. NO2 based regeneration is limited if the GPF is placed post lean NOx trap. Thus, from the above figure it is clear that there is no necessity for active regeneration with gasoline engines.

Section 2.3 Engine Emissions Modelling

Figure 14 : Comparison of Predicted and Measured CO And NOx Emissions [11]

The work [11] stands representative of the fact that phenomenological models in 1-D engine packages for CO and NOx are only marginally accurate “off the shelf”. These are used with scaling factors to achieve desired engine out levels in order to captures AFR effects in this work.

Section 2.4 On Board Diagnosis of Emission Control Devices

2.4.1 OBD in Vehicles – Brief History and State of Art

On board diagnostics is a technology that was introduced in vehicles in the late

1970s to monitor the working and diagnose the faults that could occur in various systems.

Its function also included assigning and storing a fault code to each fault noticed in the system and giving an indication to the driver about the fault. This was driven primarily by the need to ensure emissions systems continued to operate within limits as well as ensure safe operation of vehicle systems.

As electronics was starting to be extensively used in the control of various subsystems in a vehicle, the industry noticed a steep increase in complexity and sophistication. Major concern was raised in terms of service industry being able to diagnose and repair these systems. This technology was a solution to this concern as it provides the service technician an ability to get to the root of the problem by analyzing the fault codes. Another benefit of using on board diagnostics is the capability to monitor for faults during a wide spectrum of vehicle usage. This allows for detection of problem that is difficult to monitor when in a service facility. There are five key functions satisfied by any on board diagnostic system such as

 Functional monitoring

 Fault indication or warning

 Fault storage

 Default substitution 22

 Communication link

The implementation of the technology was revised and improved from time to time to meet the federal regulations and access to better tools. A brief history about the on board diagnostic systems from the past such as Pre OBD- I, OBD –I and development of OBD-

II will be explained.

The amount of diagnostic information has varied widely from the time when light was illuminated to indicate the driver of a fault to use of computers and sensors to monitor the fault. Early usage of computers on board for improving the control of fuel injection by

Volkswagen and Datsun. In 1970s saw a very preliminary implantation of diagnostic systems. These diagnostic system would essentially alert the driver of malfunction of some system in the vehicle without any further information. This light was called Malfunction

Indicator Light (MIL) or colloquially referred to as ‘Idiot Light’. In 1980s General Motors developed a diagnostic system for testing the engine control modules on the assembly line in the factory. It was called Assembly line Diagnostic Link (ALDL). It was used to monitor few systems on the vehicle and was intended for use outside the factory. The owner/driver was notified about any malfunction in the vehicle using a blinking Malfunction indicator light. This time, the pattern of blinking gave some information about the nature of the fault and it helped in faster diagnosis. Diagnostic trouble codes were introduced to communicate to the driver the nature of the fault. Pattern of blinking light helped driver decipher the trouble codes. This system also helped service technicians the ability to diagnose the failure without the use of any special equipment. The diagnostic capability varied with the

complexity of different systems and each system was capable of diagnosing at least 20 faults. These systems formed the basis for the modern On Board Diagnostic systems present in the vehicles today.

1n 1982, CARB concluded based on study that diagnostic systems are a requirement in all the vehicles to maintain the working of the emission control devices and indicate any failure to the driver for service intervention. There were two kinds of diagnostic systems found in the production cars then. These were systems that contained on board diagnostic fault codes & warning lights (GM only) and remote testers when connected gave fault codes. There was a need for standardizing the approaches into a single system adopted by all manufacturers. Thus, CARB adopted regulations for on board diagnostics to be implemented on all vehicles starting from 1988. These regulations applied to all vehicles with a three way catalyst and feedback loop for fuel injection system.

These systems were required to monitor: (1) input parameters of computer sensed emission control devices (2) functioning of fuel metering devices and (3) functioning of EGR system on vehicles equipped with and ensure its working within the design specification. The system was also expected to store trouble codes in case of any failure or malfunction and appropriately indicate the driver about it for his intervention. The regulation expected to have an on board fault isolation mechanism which identifies the likely area of the fault without the use of any external device. These regulations from this time period was named

OBD-I and it was completely implemented in all production vehicles from 1991.

More stringent diagnostic requirements was planned to be implemented in vehicles starting from 1994 model year to address the deficiencies in the OBD-I program. The goal of the current regulation was to monitor all the emission related components or systems for proper operation. The other goal was to develop a system which would result in cheaper inspection for trouble codes and reduce the frequency occurrence of the faults or under hood & tailpipe inspections for emissions. It was proposed that all vehicles would be inspected for trouble codes using a standard electrical connector before registration and all the repair has to be performed before registration. OBD-II standardized many features relating to the service and diagnostics of the vehicle. It was believed that standardization could reduce the frequency of improper repairs and lead to lower repair cost for the customers. The features of OBD-I that were standardized were the numerical trouble codes

(SAE 2012), the electrical connector (SAE J1962) and protocol (SAE J1850) used to transmit the information to an off board device. These regulations introduced then laid a very strong foundation and has been used still in the industry with modifications to suit the updated regulations [12].

2.4.2 On Board Diagnostic Regulations (Federal and California)

2.4.2.1 OBD -I

In 1985, On Board Diagnostic (OBD-I) regulations were approved by California

Air Resources Board for implementation starting from 1988 model year vehicles. As noted earlier it was introduced to monitor critical components of the emission control devices for proper operation and illuminates Malfunction Indicator Light (MIL) in case of any fault

detected. The system also provides for Diagnostic Trouble codes and fault isolation logic charts for repair technicians to diagnose the fault in engine control systems or emission control devices.

The main features of the program were to enable the engine control system monitor the following

 Major Engine sensors

 Fuel metering

 Exhaust gas recirculation function

 Circuits for open and shorts

Malfunction indicator light was introduced for the first time in this program. It was required that light be on until when the fault is detected and switched off when normal running conditions return. Light should also be turned on during ignition and turned off once the engine starts running. Serial data streams were used by some manufacturers that allows access of information about engine sensors, actuators and control systems. The data was transmitted as series of data words to be decoded by the scan tool and displayed as information about the engine operation when connected through a on board connector.

Toyota introduced this system in the vehicles starting from 1989. GM also had a similar system which was called ALDL (Assembly Line Diagnostic Link) to provide information about the engine operation in the assembly line. It was primarily used only inside the factory.

3.4.2.2 OBD-II

The diagnostics specified in the regulations were insufficient to detect all the faults in the engine due to the technical limitations at the time when it was implemented. New diagnostic technologies such as engine misfire detection, catalyst efficiency detection were introduced in production vehicles more than what was required by the regulations after

1989. This resulted in development of more comprehensive set of regulations by CARB in 1990 to effectively diagnose faults related to emission control devices that occur during normal road operation. It was decided to be phased in from 1994 to 1996 Model years. The goal of the OBD-II regulation is to provide the vehicle with an on-board diagnostic system which is capable of continuously monitoring the efficiency of the emissions control system, and to improve diagnosis and repair efficiency when system failures occur. The main features of the program were the following

 Oxygen sensor diagnostics

 Engine misfire detection

 EGR system monitoring

 Evaporative purge system monitoring

 Secondary air system monitoring

 Malfunction indicator light illumination

 Readiness test

 Storing Engine freeze frame data

 Standardization of Trouble codes, data protocol and electrical connector.

Oxygen sensor diagnostics were introduced to monitor the degradation of the sensor and lean –rich and rich lean switching times. Fuel system monitoring was introduced to monitor the abnormal operation due to altitude compensation, fuel composition, etc. When a fault is detected for more than a specified amount of time, corresponding trouble code is stored in the memory along with the details of the operating conditions in which the fault occurred. The details of the operating conditions stored is called Freeze frame data. It includes information about the engine speed, load on the engine and warmup status. Engine misfire was expected to be monitored using a crankshaft speed sensor which detects the speed of the crankshaft during every power stroke in each cylinder. Thus, when there is a misfire in one of the cylinders it is easily detected. It was also required that a trouble code was stored along with the details of engine operation. The driver is alerted using the malfunction indicator light. MIL switches rapidly on and off when there is significant misfire detected. Catalyst efficiency was expected to be monitored and faults be stored in case of significant deviation of catalyst out emissions from the specified regulations. It was implemented using two oxygen sensors (upstream and downstream) and monitoring for switching frequency of both of them. When the downstream sensor’s switching frequency reached that of upstream sensor, it is understood that the conversion efficiency of the catalyst has dropped. Readiness test was introduced as a new feature in OBD-II. Along with continuous monitoring capability, it also performs a functional test to determine the proper functioning of the components in the vehicle. This functional test is passed only after certain driving conditions such as throttle angle should have exceeded beyond a certain angle, engine must have run at a certain load, etc. These tests are a flag that indicates

on board diagnostic system is not capable of providing test data. Vehicle must be operated until all readiness test conditions have been satisfied. As discussed above, during OBD-I, each manufacturer had a way of implementing diagnostic trouble codes, data protocols and electrical connectors used to extract information from the control systems. There was need for standardization of these components of the diagnostic system and was thus implemented as part of the regulations in the OBD-II program. The standard for the electrical connector, protocol and the trouble codes was implemented and specified as part of SAE automotive standards (SAE J1962, SAE 2012 and SAE J 1850). These methods formed as foundation for the modern day diagnostic systems and have evolved from time to time as diagnosing techniques improved.

Figure 15 shows a comparison of US and EU regulations for OBD for particulate matter and the ever increasing regulation standard for PM. It can be observed that as diagnostic standards have become stricter since 2009. This requires the automakers to use better technologies to be able to diagnose failures. As an example, it can be noted that, until

2012 diagnostic standards for PM could be met by differential PM sensor, but the stricter regulation from 2012 for US and 2014 for EU require the use of better sophisticated methods such as resistance based soot sensor to detect PM emissions.

Figure 15: Comparison of US and EU OBD Regulations for PM diagnosis [13]

2.4.3 Faults Diagnostic Methods in Emission Systems

In recent years, complexity in the systems integrated into a vehicle have increased many folds. There is a major role played by the electronics in a vehicle to ensure many availability of many functions. With ever increasing complexity, it becomes difficult to detect and locate a fault without a proper system that monitors the function of various systems in the vehicle and promptly indicates the driver of any abnormal behavior. The process of detecting, isolating and identifying a fault in a system is termed diagnosis.

A diagnosis is a process in which faults are detected and isolated from actuators, sensors and working components. Diagnostics serve different functions in automotive systems such as providing safety, information on functional availability and need for system service. The three tasks involved in the diagnostic process are

 Fault Detection

 Fault isolation

 Fault identification

Following schematic illustrates the use of model-based technique in fault diagnosis.

Figure 16: Schematic of Model-Based Fault Diagnosis Process

Fault detection process involves identification of any abnormal behavior in a system that could lead to its failure. It is the basic function any diagnostic system is expected to perform. Fault isolation is the task that involves locating the component that is causing abnormal behavior. Depending on the severity of the fault, it may be informed to the driver by illuminating a light on the dashboard or trouble code may be stored in the engine control module for later diagnosis. Fault identification is the process of understanding the root cause and magnitude of the fault within the system. This is typically an offline process due to the added complexity involved in it. It is normally applied to the 31

returned parts during warranty. The techniques involved in performing fault identification closely matches with FMEA methods.

Fault is defined as malfunction of a system that causes abnormal working or loss of function. There are different ways of classifying faults. The following way classifies faults based on time of occurrence. They are

 Abrupt fault

 Incipient fault

Abrupt fault is that which occurs in the system suddenly without any prior symptoms and persists in a component. Incipient fault is that which develops slowly in a component with prior symptoms.

Another way of classifying faults is for systems using a model-based framework. There are three classifications according to this.

 Additive measure fault

 Additive process fault

 Multiplicative process fault

Additive measure faults correspond to difference in measured and true value of the plant input/output. These faults are described as sensor biases and actuator malfunction that result in deviation of intended control value changing from what is provided by the actuator. Additive process faults are those which are external and internal disturbances on the system that cause the output of the system to change. These faults can be best described

as loads and leaks in the system. Multiplicative process faults are those which cause change in system parameters due to deterioration, loss of power, surface contamination, etc.

2.4.4 Sensors for Lean Burn Engines

2.4.4.1 Particulate Matter Sensor- Resistance based

Sensor working is divided into two phases such as collection and detection of particulate matter during the sensing time. During the Sensing Time, there are two main phases of the sensor's resistance behavior: a “Dead-band” Phase - where the sensor's output

Design of Experiments not noticeably change, even though the sensor is collecting soot.

“Sensitivity” Phase is where the sensor output resistance decreases in response to increasing soot loading. “Regeneration,” in which the collected material is oxidized at high temperature and thus removed from the sensing portion of the sensor's element [14].

Figure 17: Resistive PM Sensor [14]

Figure 18: Phases of Soot Sensor Operation [14]

In the first step, the sensor's output signal is filtered to remove voltage noise and then converted to a resistance value. Secondly, the soot resistance is compensated for temperature, since the resistance of a given quantity of soot changes significantly with temperature. Thirdly, an algorithm identifies large instantaneous changes in resistance, which may be associated with large soot particles being captured or released by the sensor, and applies compensation. In the final step, the soot mass and accumulation rate are determined.

2.4.4.2 Particulate Matter sensor-Capacitance based

The sensor detects the accumulation of soot. To detect the amount of soot, the sensor operates cyclically and in three sequential stages as shown in the Figure 19. In the first measurement stage, soot is forcibly collected using an electric field and a thin soot layer is formed on the surface of the detecting electrode. In the second stage, soot is naturally accumulated and detected by measuring electrostatic capacitance changes. In the third stage, the accumulated soot is burned using a heater and the sensor returns to the first stage of the measurement [13].

Figure 19: Three Stages During Working Of Capacitance Based Soot Sensor [13]

Sensing for OBD

Measuring the impedance between the electrodes was considered as an alternative method of measuring the amount of deposited PM. The deposited PM can be simulated by the capacitance and the resistance in the equivalent circuit. A preferred detection method would be independent of temperature because the exhaust gas temperature varies widely

throughout the driving cycle or according to driving modes. As the capacitance measurement shows weaker temperature dependency than the resistance measurement, it is therefore suitable for measuring the PM amount [13].

Chapter 3 Model Development of a Flexible Vehicle Platform

Section 3.1 Introduction

The vehicle simulator used in this work is a quasi-static, energy-based, model of a conventional vehicle powered by an IC engine coupled with an automatic transmission.

This model was adopted from previous work done at CAR modelling diagnostic algorithm for diesel hybrid aftertreatment system [15]. It is termed quasi-static as dynamics exclusively occurs at the vehicle level with a relatively low bandwidth. The model lumps all the drivetrain inertias into single effective vehicle mass and neglects any driveline stiffness and assumes infinitely fast response for the powertrain actuators. At a high level, this model consists of four main components such as driver model, powertrain model, vehicle model and after-treatment model.

Figure 20: Quasi-Static vehicle simulator

It feeds back the vehicle velocity to the driver, who compares it with a desired velocity profile and actuates a brake or accelerator pedal response that then actuates the powertrain and consequently the vehicle moves. This approach to modelling is called forward simulator model. The after-treatment model consists of five main components such as map based model for emission concentration prediction, three way catalytic convertor, gasoline particulate filter, Lean NOx trap and Regeneration controller for NOx trap purge.

Emission concentration of different species is predicted using engine speed, torque and

AFR as inputs.

Section 3.2 Driver Model

The driver model observes the difference in the desired and current vehicle velocity and commands brake or accelerator pedal position according to a Proportional Integral

Derivative (PID) controller.

푡 푑((푣 −푣 ) 훼 = 푃 (푣 − 푣 ) + 퐼 ∫ 푒(푣 − 푣 ) 푑푡 + 퐷 푑푒푠 푣푒ℎ (1) 푑푟 푑푒푠 푣푒ℎ 푑푟 0 푑푒푠 푣푒ℎ 푑푟 푑푡

푡 푑((푣 −푣 ) 훽 = 푃 (푣 − 푣 ) + 퐼 ∫ 푒(푣 − 푣 ) 푑푡 + 퐷 푣푒ℎ 푑푒푠 (2) 푑푟 푣푒ℎ 푑푒푠 푑푟 0 푣푒ℎ 푑푒푠 푑푟 푑푡

Proper tuning of the gains will result in 훼 and 훽 values within the range of 0 and 1. Due to the nature of the equations above, it is made sure that both pedals are not actuated simultaneously in the model. As noted previously, this approach is known as forward modelling as it considers the current velocity of the vehicle and calculates the actuator 38

inputs to follow the desired vehicle velocity profile. On the contrary, the backward modelling approach initially considers a velocity profile and subsequently calculates the road load. A control strategy dictates a combination of powertrain actuation inputs to satisfy the road load and thus following the drive cycle. Thus, backward simulator follows the drive cycle exactly, whereas forward simulator attempts to match the desired velocity drive profile. Forward simulator model precisely represents the real world scenario and thus is the appropriate choice.

Section 3.3 Powertrain Model

The powertrain model consists of five main components such as map based engine model, internal combustion engine and torque convertor dynamics model, supervisory controller, automatic transaxle model, front & rear brakes and tire models. This model takes accelerator pedal and brake pedal input from the driver model and then feeds it as an input to the supervisory controller. The supervisory controller calculates the torque request and the selects the appropriate gear according to a control strategy modelled in

STATEFLOW in SIMULINK. The torque request is compared with the data from engine map to make sure the requested torque is well within the limits. The equations governing the dynamics of the torque convertor and transmission is used to calculate the torque at the wheel for the respective gear as selected by the controller. The brakes and tire model considers the braking proportion to the front wheels and back wheels and thus, calculates the tractive force at front and rear. These two forces are summed up to represent the tractive force for the entire vehicle.

Section 3.4 Vehicle Model

The vehicle model calculates the velocity of the vehicle given the tractive force (F) as inputs and considering opposing forces such as aerodynamic drag due to the shape of the vehicle (Fdrag), rolling resistance due to the friction between tire and road surface (Frr) and the forces due to the weight of the vehicle when moving up an incline (Finc). The equations governing this model are as seen below

푚푣푒ℎ푣̇푣푒ℎ = 퐹 − (퐹푟푟 + 퐹푑푟푎푔 + 퐹𝑖푛푐) (1)

퐹푟푟 = 푚푣푒ℎ𝑔퐶푟 cos(훼) (2)

1 퐹 = 휌 퐶 퐴 푣2 (3) 푑푟푎푔 2 푎𝑖푟 푑 푓 푣푒ℎ

퐹𝑖푛푐 = 푚푣푒ℎ𝑔 푠𝑖푛(훼) (4)

Where,

푚푣푒ℎ - Mass of the vehicle

푣푣푒ℎ - Velocity of the vehicle

𝑔 - Acceleration due to gravity

훼 - Angle of incline on which the vehicle is moving

퐶푑 - Drag coefficient of the vehicle

퐴푓 - Frontal Area of the vehicle

휌푎𝑖푟 - Density of air

Section 3.5 Aftertreatment Model

The aftertreatment model was developed integrating the models of aftertreatment devices and sensors available in the public domain. The main intent was to develop a generic tool to perform diagnostic analysis and analyze the capability of chosen sensor set to diagnose failures. This model consists of five main components such as emission concentration predictor, three way catalytic convertor, gasoline particulate filter, Lean NOx trap and AFR controller for NOx trap purging.

Figure 21 : Schematic of the Aftertreatment Model

The component selection for regulating the exhaust gas species was primarily dictated by the modes of GDI engine operation. GDI engine is known to operate in two different modes which uses homogenous and stratified air fuel mixtures. Emission concentrations of exhaust gas species change significantly with the change in mode of operation. Emissions from the stoichiometric operation of the engine can be regulated with a three way catalytic 41

convertor as it is operated primarily using stoichiometric air fuel ratios and thus, conversion efficiency CO, HC and NOx to harmless components are high enough to meet the emission standards. Once the ECU decides to operate the engine in stratified mode, the engine is fed with lean air fuel mixtures. As noted above in the Chapter 2, efficiency of three way catalytic convertor is high only in a narrow region of air fuel ratios around stoichiometric.

Thus, catalytic convertor proves to be less efficient for NOx during the lean operation of the engine to meet the regulations. Lean NOx trap is added to solve the problem of regulating NOx emissions during the lean operation of the engine. Lean NOx trap stores

NOx during lean operation and purges the trap with a rich air fuel mixtures. The trap is purged to regain the trapping efficiency using rich air fuel mixtures for combustion that results in sufficient reductant in the exhaust to reduce the NOx to N2 and in the process reductants (CO and HC) gets oxidized to CO2 and H2O.

Due to the nature of operation of the GDI engines, it is known to produce considerable amount of particulate matter which also needs to be regulated before releasing into the environment. The function of regulating PM emission is performed by particulate filter which traps the soot in the exhaust and purges the trap from time to time to maintain high trapping efficiency.

The tool developed is a co-simulation platform with Gasoline Particulate Filter model in GT-POWER and the rest of the aftertreatment model in SIMULINK. GT-

POWER was chosen for particulate filter modelling because of an existing template available in the software to model the filter. The model required geometric parameters,

concentration of emission species and exhaust gas temperatures as inputs. It had the capability to calculate the resulting gas concentration and post filter exhaust gas temperature. All the models of aftertreatment devices were integrated by calculating the resulting concentration of exhaust gas species and the temperature post each device. Based on a literature survey, it indicated the common configuration of integrating the three devices was to use the three way catalytic convertor in the close coupled position to the engine and particulate filter & lean NOx trap in the under floor position. Following sections will consist of a detailed discussion about the each component in this model.

3.5.1 Engine Emissions Model

Engine emission model predicts the ppm concentration of the exhaust gas species such as CO2, H2O, O2, N2, NO, CO, HC, SO2 and soot using map based data derived from steady state simulations using default GT-POWER engine models and brake specific emission numbers due to the unavailability of experimental data. The inputs of the model are engine speed, engine torque, AFR from the LNT regeneration controller. The outputs of the model are engine out mass fraction of different exhaust gas species, exhaust gas temperature, mass flow rate of exhaust gas species and AFR.

This model predicts CO2, O2, CO and NOx based on a map which takes engine speed, engine torque and air fuel ratio of mixture as inputs. The data in the map was assembled using the results of steady state simulations performed using default engine model templates available in GT-POWER. As discussed previously, these models effectively capture the trends and the effects of change in AFR and combustion

temperature. HC and soot emissions are based on map (that uses engine operating points to predict emission concentration) generated by assuming a brake specific number for each of the species and calibrating that parameter to reach a known tailpipe emission value in g/mile by running a model that can generate g/mile value of emission for each species on a particular drive cycle. A detailed account of simulations will be discussed in the following section. A detailed account of data generation process will be discussed in the following sections.

3.5.1.1 Data Generation using GT-Power

The model used for capturing the trends of emission concentration (ppm) of species such as CO2, O2, NOx and CO, exhaust gas temperature and air mass flow rate was an existing 2.0L GDI engine model provided by GTI-SOFT in their software as an example.

A throttle controller was added to the model to give the ability to run steady state simulation of different engine operating points and changing AFR. The controller commanded a throttle angle based on a reference torque and feedback input of brake torque and speed at which the engine was operating currently.

Figure 22 : Snapshot of GT-POWER Model Table 1 : Engine Specifications Steady state simulations were performed by running several cases changing AFR, engine speed and torque. These results were post processed using MATLAB in the required format to use in SIMULINK models. The data derived from GT-POWER were used only to capture the trends and changes in the emission concentration with changes in AFR and combustion temperatures. Scaling factors were applied to match the model results for CO and NOx emissions to trends observed in the literature. The factors were applied to match the engine out emission, g/mile values reported in the literature for FTP-75 driving cycle.

Figure 23: Engine-out CO Emissions from Two Vehicles with DI and PFI Engines on a FTP-75 drive cycle [16]

Figure 24: Engine-out NOx Emissions from Two Vehicles with DI and PFI Engines on a FTP-75 Drive Cycle [16]

The average of bag-2 and bag-3 emissions were considered as a baseline for the model results to be matched as the scope of work did not require us to model the cold start of the

engine. The following chart shows the baseline results of the model before the application of scaling factors.

FTP emissions using GT-POWER data

15 12.59

5 3.528 3.5 1.24 0.07 0.2 0 CO HC NOx Engine out (g/mile) Tailpipe (g/mile)

Figure 25: Engine-Out and Tailpipe Emission Results before Scaling

The scaling factors applied are shown in the table below and the chart shows the engine out and tailpipe out emissions after the scaling factors were applied in the model. It can be observed that the engine out NOx emission values are lower than what was observed in the literature. It has to be noted that literature results is from a 1998 model year vehicle with a GDI engine. It was observed that there were too many regeneration events triggered for purging the trap and did not meet the tier 3 emission regulations. Thus, an appropriate scaling factor was used that reduced the NOx tailpipe emissions to below the emission standard in order to be representative of a modern engine system.

Figure 26: Scaling Factors and Emission Results after Scaling

3.5.1.2 Data Generation using Brake Specific Emissions

Soot emissions data was derived from assuming a Brake Specific Particulate Matter

(BSPM-g/kWhr) emissions and scaling the BSPM quantity by running FTP-75 drive cycle so that the soot tailpipe emission is below the emissions standard of 3 mg/mile. Similarly,

HC emissions was also derived using the brake specific hydrocarbon emissions quantity from a non-proprietary engine data available at OSU-CAR. The quantities used have been summarized in the table below.

Table 2: Brake Specific Emission of HC and Soot

3.5.2 Aftertreatment Components

The three aftertreatment components in the model are three way catalytic convertor, gasoline particulate filter and a lean NOx trap and also integrated in the same order as listed. As noted above, the component selection to use lean NOx trap was dictated by the different modes of GDI engine operation and need to reduce the engine out NOx and PM emissions to meet regulation.

3.5.2.1 Lean NOx Trap (LNT)

Lean NOx trap is an after treatment device commonly used to reduce the engine out NOx emissions in lean burn and diesel engines. LNT is typically used as a storage device, capturing NOx during lean operation of the engine. The trap is regenerated by controlling the exhaust gas mixture by creating a mixture with rich AFR. As the trap is regenerated, the released NOx is catalytically converted using the reductant in the rich mixture into harmless gases before releasing into the atmosphere. The tailpipe emissions can be significantly reduced by modulating between lean and rich exhaust gas mixtures.

3.5.2.1.1 Modelling Methodology

LNT model used in this project was adopted from a previous work performed at center for automotive research [15]. This model effectively captures the physics and chemistry of the system that drives the dynamics of the system while maintaining a simple structure that would require low computational resources. A grey box modelling approach

was followed by utilizing mass and energy conservation principles while also modelling the simplified form of chemical reactions during storage and release phases of operation.

The inputs to the LNT model are exhaust gas mass flow rate, exhaust gas temperature and feed gas composition. The feed gas composition to this model is assumed to consist of eight species such as CO2, H2O, N2, O2, NO, CO, HC and SO2. The unburned hydrocarbon is assumed to have the same composition as gasoline fuel (propane) which has the molecular formula as C3H8. Another assumption made is that NOx emission from the engine primarily consists of only NO. This assumption is based on the facts that percentage of NOx emissions in spark ignited engine exhaust is less than 2 percent and the adsorption of NO is slower than the NO2 which makes NO the limiting factor.

The LNT model consists of three states that govern the dynamic phenomena representing the transient behavior of the system which are mass of stored oxygen, mass of stored NOx and catalyst temperature. An additional state, the mass of stored SO2, was added to characterize the aging and sulfur poisoning in the catalyst.

The main components that govern the operation of lean NOx trap in the model are listed as follows

 Oxygen storage and release

 NOx storage and release

 SO2 storage and release

 Temperature dynamics of the trap

Figure 27: Schematic of LNT Modelling Methodology [15]

Following sections will give a detailed account of the methodology of modelling each component listed above.

Oxygen storage and release dynamics

Oxygen Storage and release dynamics are characterized using semi empirical mathematical models that closely matches with the experimental behavior. This model is based on the approach followed to model oxygen storage model in three way catalytic convertor. The mass conservation principle is applied between amount of oxygen stored and that present in the exhaust stream. This results in a mathematical model governing the storage and release of the oxygen as shown below.

푑푀 푂2 = 푟 − 푟 (5) 푑푡 푂2,푠푡표푟푒푑 푂2,푟푒푙푒푎푠푒푑

푚̇ 푂2,표푢푡 = 푚̇ 푂2,𝑖푛 − 푟푂2,푠푡표푟푒푑 + 푟푂2,푟푒푙푒푎푠푒푑 (6)

The mass of stored oxygen is product of maximum oxygen storage capacity at that temperature and the fill ratio which is normalized to be between 0 and 1. The ratio between

amount of oxygen stored currently and the maximum storage capacity at the current conditions gives the fill ratio. As noted above, the maximum capacity of oxygen storage has been modelled to change according to temperature which is defined by the empirical relationship as follows

2 푇−푇푚 퐶푂2 = 퐶푚 exp [− ( ) ] (7) 푇푠

Where Cm, Tm and Ts are model parameters that were identified using experiments and explained in the literature [17].

As noted in the previous works [18], the oxygen storage and release have a non-linear relation with oxygen fill ratio and proportional to inlet mass flow rates of oxygen and reductants.

(1−푒훼푥) 푟 = 푘 ( + 1) 푚̇ (8) 푂2,푠푡표푟푒푑 푠푡 푒훼−1 푂2,𝑖푛

(푒−훽푥−1) 푟 = 푘 ( ) (푚̇ + 푚̇ ) (9) 푂2,푟푒푙푒푎푠푒푑 푟푒푙 푒−훽−1 퐶푂 퐻퐶

Where, 푘푠푡 and 푘푟푒푙 are two empirical constants and 훼 , 훽 are multipliers which is dependent on temperature of the catalyst.

The empirical formulation for calculating flow rate of stored O2 assumes that the amount of CO and HC present in the exhaust is almost nil. This is achieved by the use of three way catalytic convertor upstream the lean NOx trap. The conversion efficiency of CO and HC is more than 90% for lean air fuel mixtures in a TWC and thus making sure very less amount of reductants enter the LNT during storage phase [17]. The second major

assumption of this model is that oxygen storage has more precedence over the NOx storage.

It means during storage phase as the exhaust mixture is lean, if there are free sites available on the catalyst, oxygen molecules are stored first and then remaining sites are taken by

NOx molecules. Similarly, as the trap is purged with a mixture of rich exhaust gas, oxygen molecules are released first and they combine with the available reductants and converts into CO2 and H20 according to the reactions below.

1 퐶푂 + 푂 → 퐶푂 (10) 2 2 2

푚 푚 퐶 퐻 + (푛 + ) 푂 → 푛퐶푂 + 퐻 푂 (11) 푛 푚 4 2 2 2 2

The stoichiometric equations (assuming reactions at equilibrium) are used to calculate the resultant mixture after the release of oxygen from the trap according to the chemical reaction shown above.

NOx storage and release dynamics NOx storage and release dynamics have been modelled using a similar approach as used for modelling oxygen storage and release dynamics. The dynamics associated with the

NOx adsorption and release are described using a similar approach, which defines the state equation in terms of the NOx stored mass, and the NOx mass balance in gas phase in the output equation.

The mass flow rate of stored/released NOx in the catalyst depends on temperature of the catalyst, the fill ratio and inlet mass flow rate of oxygen/reductants thus making the system more nonlinear.

(1−푒훾푥) 푟 = 푘 ( + 1) 푚̇ (12) 푁푂푥,,푠푡표푟푒푑 푠푡 푒훾−1 푁푂,𝑖푛푙푒푡

(푒−휑푥−1) 푟 = 푘 ( ) (푚̇ + 푚̇ ) (13) 푁푂푥,푟푒푙푒푎푠푒푑 푟푒푙 푒−휑−1 퐶푂 퐻퐶

Where, 훾 is a multiplier that has a linear dependence on the catalyst temperature. The chemical equations that govern the storage phase of LNT are described as follows

1 푁푂 + 푂 → 푁푂 (14) 2 2 2

1 2푁푂 + 퐵푎퐶푂 + 푂 → 퐵푎(푁푂 ) + 퐶푂 (15) 2 3 2 2 3 2 2

The resulting mixture composition is calculated based on the assumption that the reaction is at equilibrium and thus calculating the amount of components produced by appropriately balancing number of moles of reactants and products.

As the NOx trap is filled, storage efficiency drops and thus resulting in low conversion efficiency of NOx. The trap is purged with rich exhaust gas mixture to regenerate the trap to improve its storage efficiency. The regeneration of the trap occurs in two phases. In the presence of reductants such as CO and HC, NOx is released from the trap. The released

NOx is catalytically reduced to harmless gases such as N2 and in the process CO and HC are oxidized into CO2 and H2O. The chemical reaction that governs the regeneration process is described as follows

퐵푎(푁푂3)2 → 퐵푎푂 + 푁푂2 (16)

퐵푎푂 + 퐶푂2 → 퐵푎(퐶푂3)2 (17)

The released NO2 from the trap disintegrates to form NO and O2. NO reacts with CO and

HC to form N2, CO2 and H2O.

1 푁푂 → 푁푂 + 푂 (18) 2 2 2

1 푁푂 + 퐶푂 → 푁 + 퐶푂 (19) 2 2 2

The mass flow rate of NO2 that is converted to N2 is calculated using the conversion efficiency factor which is dependent on catalyst temperature and mass flow rate of reductants during regeneration.

푚̇ 푁푂푥,푐표푛푣 = 푘푐표푛푣휂푐표푛푣푚̇ 푁푂푥,푟푒푙 (20)

Similarly, the resultant composition of exhaust gas mixture at the LNT outlet can be calculated using the above equations (10), (11), (18) and (19) and assuming the reaction at equilibrium to use the stoichiometric equations to calculate the number of moles produced based on the reactant quantity.

Temperature dynamics of the catalyst

The temperature dynamics of the catalyst is defined using the energy conservation equation for an open system. The catalyst brick temperature is assumed to be at the same temperature as the exhaust gas constituents due to reduced average gas velocity through the catalyst.

푑푇 1 = [푚̇ 퐶 (푇 − 푇) − ℎ퐴(푇 − 푇 ) + 푄 ] (21) 푑푡 퐶 푒푥ℎ 푝 푒푥ℎ 푊 푟푒푎푐

Where, C represents the catalyst thermal capacity (J/K), 퐶푝 is the heat capacity of the feed gas and 푄푟푒푎푐 is the heat generated from the release and conversion reactions of stored

NOx and O2.

The empirical constants and certain model parameters used in the model have been adopted from the previous work performed using the same model [15] [17].

3.5.2.2 Gasoline particulate filter

Gasoline particulate filter has been modelled in GT-POWER using the

‘DieselParticFilter’ template. This model is ‘wall’ flow model capable of accounting for clean pressure drop, soot loading, and modelling regeneration using chemical reactions & external heating and ash creation.

Figure 28: Design of a Particulate Filter [19]

3.5.2.2.1 Modelling Methodology

Soot loading in the filter is modelled in this template using deep bed filtration method. The particulate filter modelled using this approach can be visualized to be of many 56

layers of filters stacked one above the other. This model assumes that the porous substrate consists of many identical spherical collectors each of which consists of a unit collector.

The dimensions of the unit collector are determined using the porosity of the substrate and diameter of the pore. The collection efficiency of individual unit collectors vary during the soot loading and regeneration according to the local gas properties, temperature of the substrate, pressure drop inside the substrate and substrate properties such as porosity and collector diameter.

Figure 29: Schematic of the Deep Bed Filtration Method [19]

The schematic of the deep bed filtration method of modelling soot loading in the filter shows that the filter wall is divided into ‘n’ different slabs in the wall flow direction

(Longitudinal) and ‘m’ different unit collectors (number depending the level of discretization) in each slab along the channel flow direction. It is assumed that the filtration in this model is effected by interception and Brownian diffusion. Amount of soot trapped in each unit collector is given by

푚푐,푛 = 퐸1푚𝑖푛,푛 (22)

푚𝑖푛,푛+1 = 푚𝑖푛(푛−1) − 푚푐(푛−1) (23)

Where,

푚𝑖푛,푛 − Amount of soot entering the slab ‘n’

푚푐,푛 − Amount of soot retained in the slab ‘n’

푚𝑖푛,푛+1 − Amount of soot entering the slab ‘n+1’

The collection efficiency of each unit collector is given by the relation,

3휂퐷푅(1−휀𝑖)푤푠푙푎푏 퐸𝑖 = 1 − exp [− ] (24) 2휀𝑖푑푐

Where,

휂퐷푅 - Combinational collection efficiency of Brownian diffusion and directional interception

휀𝑖 - Porosity of each unit collector, dependent on the clean filter porosity.

푤푠푙푎푏 - Width of each slab

The regeneration of soot inside the filter occurs through the built in kinetic reactions described below. Since the model has been modelled based on chemical kinetics, the model allows for change in activation temperature, pre-exponent multipliers and selectivity of the reactants for both the reactions. These parameters affect the start and end of regeneration events in the filter.

1 퐶 + 푂 → 퐶푂 + 퐶푂 2 2 2

퐶 + 푁푂2 → 퐶푂 + 푁푂 (25)

푁푂 + 0.5 푂2 → 푁푂2

The Diesel Particulate Filter template has been used to model the Gasoline Particulate Filter

(GPF) for this work as the physical structure of the filter still remains the same but for the diameter and cell density. [19]

Figure 30: Schematic of GPF Modelling Methodology

The inputs to this model include mass fractions of different exhaust gas species including soot, temperature of exhaust and mass flow rate of exhaust gas species actuated from SIMULINK. The output from the GPF model are mass fraction of different exhaust gas species, total soot mass retained and temperature of post GPF exhaust gases.

3.5.2.3 Three Way Catalytic Convertor

The three way catalytic convertor is a device that reduces the NOx and oxidizes the

CO and HC at the same time. The GDI engine is known to operate in two different modes such as homogenous and stratified operation. Homogeneous mode of operation involves use of stoichiometric air fuel ratios and stratified mode of operation involves use of lean air fuel mixtures. The two competing reasons for the use of TWC with a GDI engine is the need for regulating emissions during the homogenous mode of operation and need to oxidize 90-95% of CO and HC during stratified mode of operation to facilitate storage operation in LNT. Higher NOx emission during stratified operation is reduced by the use of lean NOx trap. The conversion efficiency of all the three species is high (~ 90%) only in the narrow range of air fuel ratios at the same time. As noted in the Figure 3, conversion efficiency of CO and HC remains high during lean operation due to the presence of excess oxygen to oxidize itself with. NOx efficiency drops considerably as the air fuel mixture is made leaner.

Figure 31: Schematic of TWC Modelling Methodology In this work, TWC has been modelled using the conversion efficiency maps obtained from a previous work modelling three way catalytic convertor [18]. It is assumed that 4 equilibrium reactions described below occur in the order described and used for calculating the resultant gas mixture composition that is essentially conserving the number of moles produced and reacted.

The chemical reactions that govern the operation of TWC in the model are described as follows

푦 푦 푦 (2 + ) 푁푂 + 퐶퐻 → 퐶푂 + 퐻 푂 + (1 + ) 푁 2 푦 2 2 2 4 2

푁푂 + 퐶푂 → 0.5푁2 + 퐶푂2

1 퐶푂 + 푂 → 퐶푂 (26) 2 2 2

1 푦 퐶퐻 + 푂 → 퐶푂 + 퐻 푂 푦 4 2 2 2 2

The key assumption made in the model is NO has the highest reactivity and thus reacts with HC first and the remaining NO reacts with CO to form N2 and CO2. Though it is

known that all the reactions happen simultaneously inside TWC, this model assumes the reactions follow the order in which it is listed above.

3.5.3 Modelling and Calibration of AFR controller for LNT Regeneration

Figure 32: AFR Controller Model

AFR controller for LNT regeneration works to maintain high storage efficiency in the catalyst while monitoring the conversion efficiency between the engine out NOx emission and the LNT out NOx emission. The AFR controller triggers a regeneration event as it detects the NOx conversion efficiency to drop below a certain threshold. As the regeneration event is triggered, the controller commands a rich mixture of air and fuel for the engine operation. This is a simplistic controller designed as a placeholder for more complicated, proprietary controllers within the CAR industrial consortium.

Table 3: Conditions for Start and End of LNT Regeneration Events

This creates sufficient reductants to be produced in the exhaust, which makes the catalyst release the stored NOx and catalytically convert it into N2 and CO2. As this happens, when the fill ratio of the both oxygen storage and NOx trap reduces below a certain threshold, Regeneration event is ended. This is due to the fact that the stored NOx and oxygen has been released from the catalyst. The controller has been modelled using logic states such as 0 and 1. Regeneration event is triggered by 1 and ended with 0. Initially, threshold of 90% NOx conversion efficiency was used to trigger the regeneration events, it was observed that the cumulative NOx efficiency dropped significantly and thus the tailpipe emissions. The calibration of this parameter by trial and error led to increased cumulative efficiency and reduced tailpipe emissions.

3.5.4 Model Application - Fault Modelling Approach

The after-treatment model was added with an additional capability to inject faults into the system to conduct diagnostic analysis. Based on the literature survey, faults pertaining to each aftertreatment system was identified and certain ones were selected to be included

in the model-based on the scope of the work. The faults considered were classified into three types such as

 Actuator faults

 System faults

 Sensor faults

Figure 33: Schematic of Fault Modelling Approach

3.5.4.1 Actuator Faults

Actuator faults are those which simulate the change in emission concentration of exhaust gas species due to a fault in the engine actuators. As noted above, the emission concentration predictor model is map based, faults were modelled using multiplicative factors that appropriately alter the emission concentration predicted by the maps.

3.5.4.2 System Faults

System faults considered were specific only to the aftertreatment model due to the scope of the study limited diagnostic analysis of aftertreatment analysis. These faults simulate the defective aftertreatment devices with the use of which results increased tailpipe emissions. The following sections detail the fault modelling approach followed for each component.

3.5.4.2.1 Three Way Catalytic Convertor

Faults considered in the three way catalytic convertor simulate the change in conversion efficiency of CO, HC and NOx. As the model is based on conversion efficiency maps adopted from literature, user scalable factors have been used that essentially modifies the conversion efficiency predicted by the maps.

3.5.4.2.2 Gasoline Particulate Filter

The two faults considered for the gasoline particulate filter was cracking/leaking of the filter and ash loading in the filter. Cracking/leaking of the filter was simulated by bypassing the flow of soot across the filter and GPF model in GT-POWER provides an option to model ash storage in the filter.

3.5.4.2.3 Lean NOx Trap

Faults considered for lean NOx trap simulate the change in the capacity of the trap to store oxygen and NOx. Trap’s capacity for storage can be modified by either multiplying a user

scalable factor for maximum storage capacity of the trap or adding the amount of sulfur in the fuel.

3.5.4.3 Sensor Faults

Sensor faults modelled were of three types such as

 Multiplicative faults (sensor gain),

 Additive faults (bias) and

 Noise which modifies the variable measured.

Multiplicative faults were modelled by having a factor multiplied to the variable measured.

Additive fault introduced a factor added to the variable measured. These factors could be varied depending the type of fault being simulated.

Chapter 4 Design of experiments for GPF and Results of Fault Injection

Section 4.1 Model analysis – GPF design of experiments

The model developed in the previous chapter was used to conduct a Design of

Experiments (DOE) by changing the design parameters of the particulate filter to study its effect on the performance of the filter and analyze the need for active regeneration. It was decided to run the vehicle on FTP-75 drive cycle with 4 substrates with different cell density and wall thickness combinations, filters with 8 combinations of diameter and length, filters with different porosity. The plan for Design of Experiments (DOE) study has been summarized in the Table 4 below.

Table 4: Design of Experiments Plan for Exploring Design Space of GPF

4.1.1 Change in Cell density/Wall thickness

Two important characteristics of the filter that define the filter’s performance to a greater extent are cell density and wall thickness. Cell density refers to the number of open cells facing the flow of soot for the soot to enter the filter. Its units are CPSI – cells per square inch. Wall thickness refers to the thickness of the filter walls through which as the soot flows gets filtered. Thus thicker the walls, filter shows better trapping abilities but with a disadvantage of increasing back pressure on the engine. Cell density and wall thickness are related terms in such a way that if the diameter of the filter remains the same, as the wall thickness increases , cell density decreases and similarly vice versa.

Table 5: Specification of Filters with Different Cell density/Wall thickness

It can be seen from the results below that as the wall thickness was increased, trapping efficiency increased. As the wall thickness increased, soot flow faces more

restriction and thus trapping efficiency increases. The filter with maximum wall thickness

(12 mil) has the highest trapping efficiency of 98.72%.

Trapping efficiency with different wall thickness 100 98.15 98.72 97

95 92.67

85 300/10 300/12 360/8 360/5 Trapping effeciency (%)

Figure 34: Impact of Trapping Efficiency with Change in Wall Thickness

4.1.2 Change in Length of the Filter

The length of the filter was changed to observe its effect on the pressure drop characteristcs of the filter when vehicle runs a FTP-75 driving cycle. As the length increases , the surface area of the filter and the channel flow resistance increases. Thus , as the length increases , the average pressure drop across the filter should be observed to increase due to higher channel flow resistance. Increase in channel flow resistance results in more soot trapped in lengthy filters.

Table 6: Specification of Filter with Different Lengths

The increased soot trapped causes higher average pressure drop across the lengthy filter. The results presented below agrees well with the phenomenon described. It can be observed that the longest filter (6 in – length) traps the highest amount of soot and thus, least tailpipe soot emissions. The average pressure drop is highest for the filter with the longest length due to the highest amount of soot trapped (6in - length).

Average Pressure drop over a FTP cycle vs Channel length 2

1.95

1.9

1.85

1.8

1.75

1.7

1.65

Average filter Pressure drop filter [kPa] Pressure Average 1.6

1.55

1.5 3 3.5 4 4.5 5 5.5 6 Channel Length [in] Figure 35: Average Pressure Drop over a FTP-75 Drive Cycle Simulation as a Function of Time for Different Lengths of Filter

Tailpipe Soot emissions with different channel length on a FTP cycle 25 Case 1 - 3in Case 2 - 4.5in Case 3 - 5in 20 Case 4 - 6in

Cummulative tailpipe emissionssoot (mg)

0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Time [s]

Figure 36: Cumulative Tailpipe Soot Emissions over a FTP-75 Drive Cycle as a Function of Time for Different Lengths of Filter

4.1.3 Change in Trap Diameter

The trap diameter of the filter was changed as shown in the Table 7 to observe its effect on the pressure drop characteristics and the amount of soot retained. The cross sectional area of the filter is increased with the increase in diameter of the filter. As the cross sectional area of the filter is increased, average pressure drop across the filter decreases even though the amount of soot trapped increases. This is due to the fact that as the cross sectional area increases, channel flow resistance decreases for the same cell density.

Table 7: Specifications of Filters with Different Diameters

The results shown below well agrees with the phenomenon described above. The filter with the highest diameter (6.7 in) traps the maximum amount of soot and has the least average pressure drop.

Average Pressure drop over a FTP cycle vs Filter diameter 2.6

2.4

2.2

1.8

1.6

1.4

1.2

Average filter Pressure drop filter [kPa] Pressure Average

0.8 4 4.5 5 5.5 6 6.5 7 Filter Diameter [in]

Figure 37: Average Pressure Drop over a FTP-75 Drive Cycle Simulation as a Function of Time for Different Diameters of Filter

Tailpipe Soot emissions with different filter diameters on a FTP cycle 20 Case 1 - 4in 18 Case 2 - 4.65in 16 Case 3 - 5.65in Case 4 - 6.6in 14

Cummulative tailpipe emissionssoot (mg) 2

0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Time [s]

Figure 38: Cumulative Tailpipe Soot Emissions over a FTP-75 Drive Cycle as a Function of Time for Different Diameters of Filter

4.1.4 Change in Porosity

Porosity is a measure of the empty space in the filter material for the soot to flow through it. It is described as a fraction of empty volume over the total volume or as a percentage between 0 and 100%. The change in porosity of the filter simulated a change in the material characteristics of the filter. Porosity of the filter was changed as shown in the table below. As it has been defined above, increase in porosity increases the free space in the filter material. This reduces the channel flow resistance allowing more soot to flow through the material with high porosity and thus reducing trapping efficiency. This phenomenon is observed in the results shown. The filter with the highest porosity (68%) has the least trapping efficiency and filter with the lowest porosity (48%) has the highest trapping efficiency.

Table 8: Specification of Filter with Different Porosity

Trapping efficiency variation with different pore sizes 100 99 98 97.28 96 95.1 94 92 48% and 0.012mm 58% and 0.017mm 65% and 0.022mm Trapping effeciency (%)

Figure 39: Trapping Efficiency as a Function of Porosity of the Filter

4.1.5 Passive Regeneration

An important observation about the regeneration of soot was consistent with all the results of the different cases in Design of Experiments (DOE) study has been described as part of the results of the Design of Experiments (DOE) study. The soot loading in the filter is very low and thus, the observed pressure drop across the filter is negligible during one drive cycle simulation. It can also be observed from the Figure 40 that soot oxidizes as soon as the filter temperature reaches around 450°C and continues to regenerate until a favorable temperature is maintained inside the filter. It is known that the gasoline engine exhaust temperatures are aggressive and always around 450 – 600°C which makes way for passive regeneration of soot inside the filter during normal operation. This leads to an important observation that there is no requirement of external heating to actuate regenerations inside the filter. As the filter temperature is maintained in the suitable range for regenerations, it continuously regenerates. The continuous regenerations results in higher ash storage inside the filter in the long run. The back pressure due to the filter design 75

of experiments not have much impact on the operation of the engine because of low soot loading and thus less impact on fuel consumption. As the soot loading is low, compared to diesel engines, smaller filters can be used. Due to the use of smaller filters, the impact of ash storage during long run might be a significant issue in the selection of the filter and needs more experimental investigation.

7 1000

6 900

5 800

4 700

3 600

Temperature [K] Temperature 2 500

1 400

Total Total soot mass in the GPF retained [mg]

0 300 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Time [s] Figure 40: Soot Mass Retained in the Filter and Filter Substrate Temperature Over a

FTP-75 Drive Cycle

Section 4.2 Sample Model Results

The sample model results shown here are representative of the model’s capability and working. The results shown here are:

 Engine out NOx and tailpipe out NOx emissions

 Cumulative NOx efficiency of the system

 NOx fill ratio of the catalyst

Cummulative NOx emissions over a FTP-75 drive cycle 15

5 Engine out Tailpipe

0 Cummulative NOx Cummulativeemissions NOx [g] 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Time [s] Figure 41 : Cumulative NOx Emission on a FTP-75 Drive Cycle

Cumulative NOx conversion effeciency 100

95 NOx conversion effeciency [%] effeciency conversion NOx 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Time [s] Figure 42: Cumulative NOx conversion efficiency of this system on a FTP-75 Drive Cycle

NOx fill ratio vs time for FTP-75 drive cycle 0.5

0.4

0.3

0.2

NOx fillNOx ratio 0.1

0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Time [s] Figure 43: NOx fill ratio during a FTP-75 Drive Cycle

NOx sensor output over a FTP-75 drive cycle 1500 Engine out 1000 Tailpipe

500

0 NOx sensor output [ppm] output sensor NOx 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Time [s] Figure 44: NOx Sensor Output

AFR sensor output over a FTP-75 drive cycle 20 Engine out Post TWC 18

AFR sensor output sensor AFR

12 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Time [s] Figure 45: AFR sensor Output

8 x 10 Resistance of soot from a soot sensor for 3 FTP-75 drive cycles 10

Resistance [ohm] Resistance 2

0 0 1000 2000 3000 4000 5000 6000 Time [s] Figure 46: Pre-GPF Soot Sensor Output

4.2.2 Fault Injection and Analysis The results shown here represent the ability of the model to be used for fault diagnosis algorithm development. Two of the faults listed in the section 3.5.4 have been considered to show that with introduction of such faults, it results in higher tailpipe emissions. 78

4.2.1 Engine Actuator Fault

The situation where a particular actuator in the engine is faulty and causing higher NOx emissions has been simulated by multiplying an appropriate factor increasing the engine out NOx emissions. It can be observed in the Figure 48 that as the NOx emissions increased, frequency of regeneration has increased significantly. It fills up quickly and regenerates to maintain the trapping efficiency. Due to the presence of too much NOx, tailpipe emissions also increases due to the NOx slipping without being trapped.

ENgine out NOx emissions with and without an actuator fault 4000 Without Fault With Actuator Fault 3000

2000

1000 NOx emissions NOx [ppm]

0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Time [s]

Figure 47: Engine out NOx Emissions With and Without Fault

NOx fill ratio with and without a fault 0.5 Without Fault 0.4 With Actuator Fault

0.3

0.2 NOx fillNOx ratio 0.1

0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Time [s] Figure 48: NOx Fill Ratio With and Without Fault

Tailpipe out NOx emissions with and without an actuator fault 2000 Without Fault With Actuator Fault 1500

1000

500

NOx emissions NOx [ppm]

0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Time [s] Figure 49: Tailpipe-out NOx Emissions With and Without Fault

4.2.2 Multiplicative Sensor Fault in a AFR Sensor

A sensor fault was simulated by changing the sensor gain which would appropriately modify the sensor output. Engine-out AFR sensor’s gain was modified to simulate faulty behavior. As seen in the results below, the multiplicative fault causes a change in the output of the sensor.

AFR sensor output with and without multiplicative fault 26 Without fault 24 With multiplicative fault

AFR 18

12 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Time [s]

Figure 50: AFR Sensor Output With and Without Fault

The simulation of faulty behavior from a defective component can be used to conduct diagnostic analysis to determine if the chosen sensor set will be able to diagnose the

particular fault simulated and also aid in development of algorithm for fault signature analysis.

Chapter 5 Conclusions and Future work

Section 5.1 Conclusions

A flexible full-vehicle model was developed with all the appropriate after treatment components for a GDI engine such as Three Way Catalytic convertor (TWC), Gasoline

Particulate filter (GPF) and Lean NOx Trap (LNT) and all the appropriate sensors. The model developed is a co-simulation platform as TWC and LNT model is modelled using

SIMULINK and GPF is modelled using diesel particulate filter template in GT-POWER.

TWC, GPF and LNT models calculate the resultant gas composition using stoichiometric mass balance equations assuming equilibrium concentrations. The resultant gas composition calculation post each after treatment device paves way for integration of all the components. This model is easily scalable across various vehicle platforms by changing the appropriate parameters. The platform developed is also given an additional capability for injecting faults to conduct diagnostic studies using this platform in the future. The modelling approach used in the work gives the user the ability to:

 Modify the vehicle parameters such as vehicle weight, drag coefficient ,

accessory loading , tire size to simulate different vehicle platforms

 Change the configuration of the after treatment system by interchanging the

components

 Modify the length of the pipes between each components

 Change the characteristics such as range of measurement of different

sensors

 Modify the conversion efficiency maps to suit a particular TWC

 Modify design parameters of the particulate filter to suit different platforms

 Modify the maximum capacity of the NOx trap to suit different engines

 Inject additional faults in the system and study the effect on diagnostic

capability of the chosen sensor set.

 Develop diagnostic monitors for different parameters using sensor outputs

for use in diagnostic algorithm that analyses the signatures generated.

A Design of Experiments (DOE) was conducted to explore the design space of GPF and study the impact of geometric and material properties on the performance parameters of the filter. The sizing of the filter for a particular engine is dictated by the availability of particular size and the spatial considerations. However from this study, it can be concluded that the size of the filter for gasoline engines can be considerably smaller due to low soot loading. The important observation from the study was the occurrence of passive regeneration of soot without any external heating source. This is due to the aggressive temperatures observed in the exhaust of spark ignited engines. The need for regeneration during short trips or during cold climates can be met using in cylinder techniques to increase the exhaust gas temperature. Smaller filter and continuous regeneration of soot inside the filter may lead to another significant problem of ash storage which may cause

excess back pressure and also reduce the trapping efficiency of the filter. This potentially may be of concern in the long run. Durability studies may need to be conducted to validate the ash storage problem.

The system efficiency observed for the conversion of CO, HC and NOx are 82%, 96% and

95.9% respectively for FTP-75 drive cycle. The tailpipe CO, HC + NOx, PM emissions observed during one FTP-75 drive cycle simulation are 1.2 g/mile, 0.13 g/mile and 2.1 mg/mile respectively. Thus, it is evident that this system configuration has the capability to reduce the engine out emissions to meet the tier 3 emission regulations.

Comparison of Tier 3 regulations and model results 6 4.2 4 3 2.1 2 1.2 0.16 0.09 0 CO NMOG+NOX PM Tier 3 (g/mile) Model results (g/mile)

Figure 51: Comparison of Model Results Vs Tailpipe Emissions from the Model

Overall System Efficiency 100 96.2 95.9

90 82 80

70 CO HC NOx System Effeciency (%)

Figure 52: Overall Conversion Efficiency of All Species for the Considered System

Section 5.2 Future Work

Few suggestion have been made as follows:  Modelling of more fault cases

 Addition of detection algorithm to analyze the faults injected and determine the

suitability of chosen sensor set for the system considered

 Refinement of models to match the proprietary experimental data.

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