Powertrain Sizing and Energy Usage Adaptation Strategy

POWERTRAIN SIZING AND ENERGY USAGE ADAPTATION STRATEGY

FOR PLUG-IN HYBRID ELECTRIC VEHICLES

A Thesis

Presented to

The Graduate Faculty of The University of Akron

In Partial Fulfillment

of the Requirements for the Degree

Master of Science

Soumendu Chanda

May, 2008

POWERTRAIN SIZING AND ENERGY USAGE ADAPTATION STRATEGY

FOR PLUG-IN HYBRID ELECTRIC VEHICLES

Soumendu Chanda

Thesis

Approved: Accepted:

______Advisor Department Chair Dr. Iqbal Husain Dr. Jose A. De Abreu Garcia

______Committee Member Dean of the College Dr. Robert J. Veillette Dr. George K. Haritos

______

Committee Member Dean of the Graduate School Dr. Malik Elbuluk Dr. George R. Newkome

______Date

ABSTRACT

An energy usage adaptation (EUA) strategy to manage the charge/discharge profile of the energy storage system for plug-in hybrid vehicles is presented in this thesis. The objective of the EUA strategy is to bring the stored energy to a low level at the end of the daily drive cycle, and to limit the number of deep discharge cycles. The EUA algorithm first predicts the energy usage for a given day based on historical usage data. The predicted energy is then compared with the actual energy used and the battery energy available to set the SOC limits in the energy management algorithm. The EUA strategy has been tuned and tested using simulations of both a series and a series-parallel plug-in hybrid vehicle (model) with vehicle control algorithms developed for the purpose. The strategy is shown to improve the fuel economy of the vehicle and to reduce the cost per mile of operation by efficiently using the off board supplied energy. It also helps to extend the life of the battery by limiting the number of deep discharge cycles to no more than one per day. A well-to-wheel analysis of the designed plug-in hybrid is also done using the standard GREET model and through vehicle simulation to investigate the overall efficiency of plug-in hybrid vehicles. The well-to-wheel efficiency of the plug-in hybrids is found to be lower than those of the conventional gasoline and electric vehicles.

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DEDICATION

Dedicated to my family and teachers

ACKNOWLEDGEMENTS

I wish to express my deep sense of gratitude and indebtedness to my academic advisor,

Dr. Iqbal Husain for his great efforts and lot of enlightening ideas during the course of this research. I also wish to thank Dr. Robert J. Veillette, for his constant support and advice during the course of my research activities. I also wish to express my sincere appreciation to Dr. Malik Elbuluk for being in my advisory committee and for his support in making this research a success.

I would like to thank my colleague Gregory Pasquesoone for his help in modeling the vehicle. I also wish to thank Gokhan Sen and Kwadwo Safo who were always there when

I needed them. I am thankful to Nayeem for his quick and sharp comments which always had a touch of experience behind them.

Finally, I would wish to thank my parents, my brother and sister for their love and encouragement over the years.

TABLE OF CONTENTS

Page

LIST OF TABLES ix

LIST OF FIGURES xi

CHAPTER

I. INTRODUCTION 1

1.1 History of Grid-Connected Vehicles 3

1.2 Plug-In Hybrid Architectures 5

1.3 Motivation for Research 6

1.4 Overview of Research 7

II. PLUG-IN HYBRID ELECTRIC VEHICLES 9

2.1 Hybrid Vehicle Configurations 9

2.2 PHEV Ratings 11

2.3 Advantages and Disadvantages 11

2.4 Well-to-Wheel Efficiency 14

2.5 PHEV Operating Strategy 17

2.6 PHEV Vehicle Components 18

2.6.1 Energy Storage System: Battery 18

2.6.2 Electric Traction Motor 22

2.6.3 Electronic Circuitry: Power Converters 23

2.6.4 Internal Combustion Engine 24

2.6.5 Generator 24

2.7 Market Status 25

2.8 Intelligent Energy Management 27

2.9 Conclusions 30

III. SERIES PHEV COMPONENT SIZING AND VEHICLE CONTROL 31

3.1 Sizing of Components 32

3.1.1 Traction Motor Power 33

3.1.2 Engine and Generator Power 36

3.1.3 Battery Sizing 37

3.2 Control Strategy 39

3.3 Drive Cycle Simulation 42

3.4 Conclusion 45

IV. SERIES – PARALLEL PHEV COMPONENT SIZING AND VEHICLE CONTROL 47

4.1 Sizing of Components 48

4.1.1 Traction Motor Power 51

4.1.2 Generator Power 52

4.1.3 Battery Sizing 53

4.2 Control Strategy 55

4.3 Drive Cycle Simulation 60

4.4 Conclusion 65

V. PHEV ENERGY USAGE ADAPTATION 66 vii

5.1 Energy Usage Adaptation 68

5.1.1 Prediction Algorithm 69

5.1.2 Energy Management Algorithm 87

5.2 Results from Energy Usage Adaptation Strategy 91

5.3 Conclusions 102

VI. ANALYSIS OF ENERGY ADAPTATION 104

6.1 Fuel Economy and Cost Per Mile 104

6.2 Results: Customer Perspective 106

6.3 Result: Ecological Perspective 120

6.4 Summary of Results 124

VII. CONCLUSIONS AND FUTURE WORK 125

7.1 Summary and Conclusions 125

7.1.1 Plug-in HEV Designs 126

7.1.2 Energy Usage Adaptation 127

7.1.3 Well-to-Wheel Efficiency 129

7.2 Contributions of Research 129

7.3 Future Works 129

REFERENCES 131

APPENDICES 134

APPENDIX A. SERIES PHEV MODEL 135

APPENDIX B. SERIES-PARALLEL PHEV MODEL 140

APPENDIX C. PREDICTION ALGORITHM CODE 146

APPENDIX D. ENERGY MANAGEMENT ALGORITHM 154

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LIST OF TABLES

Table Page

1.1 Proved oil reserves in 2006 [3] 2

2.1 Vehicle Technologies, Passenger Cars: Well-to-Pump Energy Consumption and Emissions 16

3.1 Series plug-in hybrid vehicle specifications 35

3.2 Series plug-in hybrid vehicle component data 39

4.1 Series-parallel plug-in hybrid vehicle specifications 49

4.2 Drive cycle data 53

4.3 Series plug-in hybrid vehicle component data 55

5.1 Main memory data for moving average based prediction 84

5.2 Main and Exception memory data for exception algorithm 85

5.3 Actual energy and predicted energy from Day 12 to Day 22 85

5.4 Initial input data for last eleven days 92

5.5 Input data for the next eleven days 92

6.1 Simulation results for series plug-in HEV with adaptation and no-adaptation technique 111

6.2 Simulation results for series-parallel plug-in HEV with adaptation and no-adaptation technique 112

6.3 Fuel economy and Cost per mile for series-plug-in HEV 115

6.4 Fuel economy and Cost per mile for series-parallel plug-in HEV 118

6.5 Well-to-tank efficiency calculations for the series plug-in HEV 121

6.6 Tank-to-wheel efficiency calculations for the series plug-in HEV 122

6.7 Fuel economy and cost per mile for series and series-parallel PHEV 124

LIST OF FIGURES

Figures Page

2.1 Incremental cost and annual petroleum consumption of plug-in hybrids [2] 13

2.2 Processes involved in well-to-wheel efficiency calculation 14

2.3 Battery life cycles for different depth of discharge 22

2.4 Block diagram of a motor drive system 23

2.5 Block diagram of the intelligent energy management architecture (IEMA) [7] 28

2.6 Intelligent controller based on future vehicle states 29

3.1 Block diagram of a series plug-in hybrid vehicle configuration 31

3.2 Tractive force, power and velocity profile of the series plug-in hybrid vehicle 35

3.3 Traction power requirement at different gradient and vehicle speed 37

3.4 Battery energy consumed for 0-60 mph acceleration demand 38

3.5 Block diagram of communication network of vehicle controller 41

3.6 SOC control strategy 41

3.7 Velocity profile of the series plug-in hybrid vehicle in the acceleration cycle 42

3.8 Electric motor output power for maximum (sustained) acceleration 43

3.9 Steady speed drive cycle for series mode testing 44

3.10 Battery state of charge during series-mode operation 44

3.11 Component power outputs during series mode operation 45

4.1 Block diagram of the series-parallel plug-in hybrid vehicle configuration 47

4.2 Tractive force, power and velocity profile for the series-parallel plug-in hybrid vehicle 50

4.3 Traction power requirement at different gradient and vehicle speed 51

4.4 Acceleration test results with 42-kW engine and different motor ratings 52

4.5 Electrical energy consumed during acceleration 54

4.6 SOC control strategy 56

4.7 Flowchart diagram for mode selection strategy 58

4.8 Acceleration pedal response for acceleration test 58

4.9 Velocity profile of the series-parallel plug-in hybrid vehicle in the acceleration cycle 60

4.10 Motor and engine power during acceleration 61

4.11 Steady-speed drive cycle for series mode testing 61

4.12 Battery state of charge during series-mode operation 62

4.13 Component power outputs during series mode operation 62

4.14 Velocity profile for the multi-mode test 64

4.15 Subcomponent output power for the multi-mode test 64

5.1 State of charge profile in the HWFET cycle 67

5.2 State of charge profile in HWFET_2 cycles 68

5.3 Flowchart diagram for moving average based prediction algorithm 73

5.4 Flowchart diagram for exception based prediction algorithm 77 xii

5.5 HWFET cycles with variation 79

5.6 UDDS cycles with variation 79

5.7 Actual and predicted energy usage graph for eleven days using prediction algorithm based on moving average, Test 1 80

5.8 Actual and predicted energy usage graph for eleven days using prediction algorithm based on exception, Test 1 80

5.9 Actual and predicted energy usage graph for eleven days using prediction algorithm based on moving average, Test 2 83

5.10 Actual and predicted energy usage graph for eleven days using prediction algorithm based on exception, Test 2 83

5.11 Actual and predicted energy usage graph for eleven days using prediction algorithm based on moving average, Test 3 84

5.12 Actual and predicted energy usage graph for eleven days using prediction algorithm based on exception, Test 3 84

5.13 Flowchart for the energy management algorithm 88

5.14 State of charge profile for ̿- ,0$- ƙ ̿// -4 89

5.15 State of charge profile for ̿- ,0$- > ̿// -4 89

5.16 State of charge profile using adaptation for long drive cycle 90

5.17 Eleven days energy usage pattern and predicted energy for Day 12 93

5.18 State of charge profile on Day 12 93

5.19 Eleven days energy usage pattern and predicted energy for Day 13 94

5.20 State of charge profile on Day 13 94

5.21 Eleven days energy usage pattern and predicted energy for Day 14 95

5.22 State of charge profile on Day 14 95

5.23 Eleven days energy usage pattern and predicted energy for Day 15 96

5.24 State of charge profile on Day 15 96

xiii

5.25 Eleven days energy usage pattern and predicted energy for Day 16 97

5.26 State of charge profile on Day 16 97

5.27 Eleven days energy usage pattern and predicted energy for Day 17 98

5.28 State of charge profile on Day 17 98

5.29 Eleven days energy usage pattern and predicted energy for Day 18 99

5.30 State of charge profile on Day 18 99

5.31 Eleven days energy usage pattern and predicted energy for Day 22 100

5.32 State of charge profile on Day 22 101

5.33 Predicted energy vs. Actual energy used 101

6.1 Simulation result of the series plug-in hybrid with adaptation strategy in the HWFET cycle 108

6.2 Simulation result of the series-parallel plug-in hybrid with adaptation strategy in the HWFET cycle 109

6.3 Predicted and actual energy consumption during adaptation 111

6.4 Fuel economy and end of cycle state of charge for adaptation and non adaptation rule 112

6.5 Predicted and actual energy consumption during adaptation 113

6.6 Fuel economy and end of cycle state of charge for adaptation and no-adaptation algorithms 113

6.7 State of charge profile for the series plug-in HEV without adaptation during the UDDS cycle 115

6.8 State of charge profile for the series plug-in HEV with adaptation during the UDDS cycle 115

6.9 State of charge profile for the series plug-in HEV without adaptation during the HWFET cycle 116

6.10 State of charge profile for the series plug-in HEV with adaptation during the HWFET cycle 116

xiv

6.11 State of charge profile for the series plug-in HEV without adaptation for four consecutive HWFET cycles 116

6.12 State of charge profile for the series plug-in HEV with adaptation for four consecutive HWFET cycle 116

6.13 State of charge profile for the series plug-in HEV without adaptation for a combination of HWFET and UDDS cycle 117

6.14 State of charge profile for the series plug-in HEV with adaptation for a combination of HWFET and UDDS cycle 117

6.15 State of charge profile for the series-parallel plug-in HEV without adaptation during the UDDS cycle 118

6.16 State of charge profile for the series-parallel plug-in HEV with adaptation during the UDDS cycle 118 . 6.17 State of charge profile for the series-parallel plug-in HEV without adaptation during the HWFET cycle 119

6.18 State of charge profile for the series-parallel plug-in HEV with adaptation during the HWFET cycle 119

6.19 State of charge profile for the series-parallel plug-in HEV without adaptation for four consecutive HWFET cycles 119

6.20 State of charge profile for the series-parallel plug-in HEV with adaptation for four consecutive HWFET cycles 119

6.21 State of charge profile for the series-parallel plug-in HEV without adaptation for a combination of HWFET and UDDS cycle 120

6.22 State of charge profile for the series-parallel plug-in HEV with adaptation for a combination of HWFET and UDDS cycle 120

6.23 Well-to-wheel efficiency of the series plug-in HEV 123

CHAPTER I

INTRODUCTION

The present day automobiles powered by heat engines have become an integral part of our everyday life. However, the large numbers of automobiles produced and operated are causing deterioration in the air quality, contributing to global warming and decreasing the fossil based energy resources. These environmental concerns have stimulated interests in building safe, clean and highly efficient vehicles. Electric vehicles, hybrid vehicles and plug-in hybrid vehicles are some of the alternatives for future transportation. Studies have also shown that immediate commercialization of hybrid vehicles and fuel cell vehicles can slow down the depletion of the precious oil reserves of the world [1, 2].

The number of years that the oil resources of the Earth can supply our needs depends on the discovery of new oil reserves and cumulative oil production and consumption. Historical data have shown that the consumption rate is higher than the rate at which new oil reserves are discovered. Table 1.1 shows the proven reserves of oil in

2006 and the Reserves to Production (R/P) ratio. Proven reserves are defined as “those quantities that geological and engineering information indicates with reasonable certainty can be recovered in the future from known reservoirs under existing economic and operating conditions.” The R/P ratio indicates the number of years the proven reserves

1 would last if the production were to continue at its current level [1]. The current trend shows that with the current rate of discovery of new oil reserves and the current consumption rate, the world oil reserve will be depleted by 2049 [3].

Table 1.1: Proved oil reserves in 2006 [3]

Oil proven reserves : 2006 R/P ratio Region (in 1000 million barrels)

North America: Total 59.9 12

S. & Cent. America: Total 103.5 41.2

Europe & Eurasia: Total 144.4 22.5

Middle East: Total 742.7 79.5

Africa: Total 117.2 32.1

Asia Pacific: Total 40.5 14

World: Total 1208.2 40.5

The development and commercialization of hybrid vehicles is one way to decrease the dependency on this precious reserve. One of the main focuses of the alternative vehicle research in recent years is the development of grid-connected hybrid vehicles commonly known as plug-in hybrid vehicles. The unique advantage of this class of hybrid vehicles is that they can be charged from the grid electricity. These vehicles can also operate for a limited range entirely in electric mode which can increase the efficiency of the transportation system.

Conventional hybrid vehicles have already started to help reduce the fossil fuel energy consumption. Based on Environmental Protection Agency data, the most efficient hybrids existing on the road today cut the gasoline consumption by around 40% compared to conventional heat engine driven cars [4]. It has been estimated that plug-in hybrids could reduce by half the remaining gasoline consumption by making use of grid electricity. On the whole, the plug-in hybrids can reduce the gasoline consumption by almost 70%. The rest of the fuel can be supplied by alternative fuels, which will eventually eliminate our dependency on oil. It is interesting to note that the plug-in hybrids mark the return of the electric vehicles, which were once the major system of transportation before the invention of gasoline engines.

1.1 HISTORY OF GRID-CONNECTED VEHICLES

The history of grid-connected vehicles can be traced back as far as the 19 th century when a Frenchman named Gustave Trouve built the first electric vehicle in 1881 [1]. The vehicle was powered by a 0.1-hp DC motor fed from lead-acid batteries and had a maximum speed of 15 km/h and a range of 15 km. Similar vehicles were built during this period but they were still inferior in comparison to the horse drawn carriages. The scenario changed in 1864 in the Paris to Rouen race where the horseless carriages, the so- called electric vehicles, covered a distance of 1135 km in 48 h and 53 min at an average speed of 23.3 km/h. This performance triggered the era of modern automobile industry.

The first gasoline-powered vehicle was developed in 1885. The following twenty years, the world witnessed a tremendous competition between the electric, steam and 3 gasoline-powered vehicles. The invention of regenerative breaking by Frenchman M.A.

Darracq in 1897 increased the limited range of the electric vehicles [1]. In 1900 the electric vehicle produced by French B.G.S. Co. was able to cover a distance of 100 miles per charge with a top speed of 45 mph . The production of electric vehicles during this period outnumbered the gasoline-powered vehicles by almost 2 to 1. The first commercial electric vehicle appeared in New York City and was operated as a taxi. But the construction of paved roads throughout Europe and America sounded the death-knell for the electric cars. The newly built paved roads favored vehicles which could run for long hours at high speed. Gasoline-powered vehicles with their easily available fuel and high speeds easily scored over the electric-powered cars. Moreover, the improvement in the mass production lowered the cost and increased the popularity of the gasoline- powered vehicles. As a result the electric vehicles started disappearing from the market around 1920’s. They re-surfaced again almost a half century later in 1960.

In the 60’s the environmental hazards caused by the emissions from the gasoline vehicles prompted the big automakers like GM and Ford to concentrate their research on the development of electric cars. The ‘Great Electric Car Race’ in the 60’s can also be considered as a big step towards this resurgence. The competition was staged between two electric vehicles developed by Caltech and MIT in August 1968 and stimulated great interests among the general public.

The next big return of grid-connected vehicle technology came in 1993 when the federal government announced the creation of the Partnership for a New Generation of

Vehicles (PNGV) consortium consisting of three big automakers in the country - General

Motors, Ford and Chrysler - along with 350 smaller firms. PNGV outlined a very 4 aggressive goal for the development of zero-emission vehicles using the plug-in and hydrogen technologies [5]. The Ford Prodigy and the GM Precept vehicles have been developed out of this effort. The plug-in hybrid technology is similar to the conventional hybrids except they can be charged from the grid like the electric vehicles.

Currently, the recent surges in oil prices and grave environmental concerns like global warming have forced automakers to develop commercially viable hybrid vehicles.

The focus is more on the development of plug-in hybrid vehicles that have the capability to reduce pollution and dependency on oil to a greater extent than the conventional hybrids available today in the market.

1.2 PLUG-IN HYBRID ARCHITECTURES

The plug-in hybrid vehicle architectures are the same as those of charge-sustaining hybrids except that their energy storage system has a power electronic interface for connection to the grid. The series architecture is the simplest architecture and is very suitable for plug-in hybrid vehicles. The series architecture delivers excellent fuel economy in urban driving conditions. The drivetrain uses an engine/generator set to extend the range of the vehicle. The drawback of this configuration is the size of the electrical traction motor, which has to be rated for the maximum power requirement of the vehicle.

The drawback of a series plug-in hybrid can be removed if the engine is used in parallel with the electric machine to supply power to the wheels. This is called a parallel

5 architecture and has the advantage of satisfying the same power requirement with a smaller electric machine.

The advantages of the series and the parallel architectures can be combined in a more complex architecture known as the series-parallel architecture. The complexity is the mechanical configuration as well as in the control strategy that has to work out the best usage of the drivetrain components.

1.3 MOTIVATION FOR RESEARCH

A plug-in hybrid vehicle requires an effective energy management strategy in order to have maximum operational efficiency. All the present conventional hybrids employ a strategy in which the energy storage system is recharged from the on-board power source.

In a plug-in hybrid vehicle, the energy storage will be recharged from the grid and also from the onboard power source during the daily drive cycle. An energy management strategy ensuring that the vehicle’s energy storage system is substantially depleted at the end of the daily drive cycle will result in high fuel efficiency and low cost per mile. This thesis develops such an adaptive energy management strategy to efficiently operate the plug-in hybrid vehicle. The strategy also aims to extend the battery life by limiting the number of deep discharge cycles.

1.4 OVERVIEW OF RESEARCH

The goal of this research is to present an adaptive energy usage strategy that will efficiently manage the charging and discharging of the battery and achieve high fuel efficiency and low cost per mile operation. The platforms for testing the adaptive strategy are two plug-in hybrid vehicle simulations, one based on the series architecture and the other based on series-parallel architecture. The two simulations are based on a mid-size four-door commercial vehicle available in the market. The simulations assume vehicle components sized for a given set of specifications and performance constraints. The design details of the two vehicles are presented in Chapter 3 and Chapter 4.

A fairly simple drivetrain control strategy is used to operate the series plug-in hybrid vehicle. The engine/generator is operated at the optimum operating point and is used only to maintain the given state of charge within a window. The control strategy used for the series-parallel configuration is more complex. The engine is used in parallel with the motor during high acceleration demand and also when the vehicle speed goes above 60 mph. Under all other conditions, the engine is loaded by the generator and the vehicle operates in the series mode. The idea is to use the zero-emission electric drivetrain path during low demands (city driving) and use the highly efficient mechanical path during continuous high power demand (highway driving). The drivetrain control strategies for the two vehicles are presented in Chapter 3 and Chapter 4.

An adaptive energy usage strategy that keeps track of the daily energy usage and then efficiently utilizes the battery available energy is proposed here. The strategy consists of two parts. The first part, called the ‘Prediction Algorithm,’ works outside the

7 main control module to predict the energy that will be required to drive the vehicle each day. The second part, called the ‘Energy Management Algorithm,’ operates within the main control module to actively select the state of charge limits of the battery based on the predicted energy requirement. The engine and the generator are operated based on this strategy. The detailed algorithm and the results are presented in Chapter 5.

The simulation results are presented in Chapter 6. For comparisons, the simulation results from the two vehicle configurations are presented for different drive cycles. The energy usage adaptation strategy is used with both vehicle configurations, and comparisons are made on the basis of the simulation results. The fuel efficiency and the cost per mile of operation achieved with the adaptation strategy are compared with those achieved with a rudimentary no-adaptation strategy. The series-parallel plug-in hybrid configuration with the adaptation strategy is shown to be the most effective in terms of component size, fuel efficiency and cost per mile of operation. At the end, the well-to-wheel efficiency of the plug-in hybrid vehicle is analyzed and compared with the conventional hybrids and other conventional vehicles. The analysis shows that the well- to-wheel efficiency of plug-in hybrid vehicles is poor compared to electric and gasoline powered vehicles.

CHAPTER II

PLUG-IN HYBRID ELECTRIC VEHICLES

2.1 HYBRID VEHICLE CONFIGURATIONS

The term ‘ Hybrid Vehicle’ is generally applied to a vehicle that uses an internal combustion engine (ICE) in the vehicle to generate electric energy “on board.” The electric energy is then supplied to traction electric motors, which can operate independently or in association with the ICE to power the wheels of the vehicle [1].

Hybrid electric vehicles are broadly classified into three categories: series hybrid, parallel hybrid and series-parallel hybrid [1].

A series hybrid is one in which only one energy converter can provide propulsion power. The ICE acts as a prime mover in this configuration to drive an electric generator that delivers power to the battery or energy storage system and the propulsion motor. The advantage of a series hybrid is its simple drivetrain and the flexibility of operating the

ICE at its maximum efficiency operating point. The major disadvantage is that the propulsion motor must be sized to provide the full power for vehicle propulsion under all conditions.

In a parallel hybrid, more than one energy source can provide propulsion power to the wheels. The ICE and the electric motor are configured in parallel, with a mechanical coupling that combines the torque coming from the two sources. The advantage of a 9 parallel hybrid configuration is that only two propulsion components are needed, and both the motor and the ICE can be downsized. The control complexity of this configuration is high compared to the series hybrid.

A series-parallel hybrid is a combination of a series and a parallel hybrid vehicle.

It combines the advantages of the series and parallel hybrid electric vehicles. In this configuration the ICE can charge the batteries, and also provide propulsion power to the wheels. Toyota Prius is an example of series-parallel hybrid. The drivetrain consists of a power split device that distributes the ICE power to the front wheels and the generator depending on the driving profile. The generator is used to charge the battery pack.

Hybrid vehicles are also classified based on their energy storage systems as either normal hybrid or plug-in hybrid vehicles (PHEV). Either of these two types of vehicles can have a series, parallel or series-parallel configuration. A normal hybrid vehicle has a small energy storage system which can be charged onboard. The vehicle operates in the battery mode for shorter periods and then switches to hybrid mode for rest of the cycle. A plug-in hybrid vehicle on the other hand has a large energy storage system which can be charged onboard and also from an electrical outlet. The vehicle can operate in a battery mode for longer period than the normal hybrid vehicles. The state-of-the-art in plug-in hybrid technology will be discussed in the following sections.

2.2 PHEV RATINGS

The plug-in hybrid vehicle is intended to operate as a pure battery electric vehicle for reasonable distances during the daily commute. The ICE is used to provide additional power and range for long distance driving. This type of vehicle is also sometimes known as a ‘range extender.’ The energy obtained from the external power grid in the plug-in

HEV displaces the energy that would otherwise be obtained by burning fuel in the vehicle’s ICE. This results in higher fuel efficiency in comparison to other hybrid vehicles.

A plug-in hybrid is generally rated based on the distance it travels using the off- board electrical power and is designated as ‘ PHEV X ’ where ‘X’ is the distance traveled in miles using off-board electrical energy. This range of travel where the ICE is not used is known as zero-emission vehicle (ZEV) range. A PHEV60 is a plug-in hybrid with a useable energy storage equivalent to 60 miles of driving energy on a reference driving cycle. The PHEV60 can displace petroleum energy equivalent to 60 miles of driving on the reference cycle with off-board electricity.

2.3 ADVANTAGES AND DISADVANTAGES

A plug-in hybrid vehicle has the following advantages:

Low fuel consumption

Reduced emissions

Low maintenance cost

Ability to charge from grid.

The plug-in hybrid vehicle uses the battery most of the time during the drive cycle due to the use of the high capacity batteries. This reduces the emissions, and air and noise pollution. The maintenance cost of a plug-in hybrid vehicle is low since the vehicle is primarily electric.

A plug-in hybrid vehicle can also be used to even out electricity demands during peak load demand on the grid. Excess battery charge from the plug-in hybrid can be used to send power back to the grid during peak periods. This is known as ‘Vehicle-to-Grid’ power or V2G. The vehicle can be recharged again during off-peak periods.

Compared to conventional gasoline and HEVs, plug-in hybrid vehicles have the following disadvantages:

Extra weight

High cost

Fuel economy depends on vehicle use

Poor well-to-wheel efficiency.

The battery in the plug-in hybrid vehicle increases the weight of the vehicle. The extra weight reduces the performance of the vehicle and components have to be sized for higher power rating to achieve good performance. The batteries and the electronic circuitry to charge those batteries are expensive and as a result the cost of the vehicle is also high.

The electric range of the plug-in hybrid vehicle depends on the capacity of the energy storage system. A longer electric range requires larger energy storage capacity which increases the cost of the vehicle. Figure 2.1 shows the reductions in annual petroleum consumption and incremental costs for different range of plug-in hybrid vehicles [6]. The electric range has been calculated in the Urban Dynamometer Driving

Schedule (UDDS).

Reduction in fuel consumption vs Powertrain cost increment - Midsize Sedans 14000 PHEV60 12000

10000 PHEV40 8000 PHEV20 6000

4000 PHEV5 Retail Cost Increment ($) Increment Cost Retail 2000 HEV 0

0 190 205 220 250 300 350 380 400 420 Reduction in Annual Petroleum Consumption (gals) Figure 2.1: Incremental cost and annual petroleum consumption of plug-in hybrids [6]

The graph in Figure 2.1 shows that by increasing the electric range of the plug-in hybrids, the annual reduction in petroleum increases. A PHEV40 will consume less petroleum annually than PHEV5 or HEV0. However, the decrease in petroleum consumption comes with an increase in incremental cost of the vehicle. A PHEV60 can cost US $12000 to US

$18000 more than a conventional vehicle [6].

The mileage gained using batteries is dependent on the way the vehicle is used.

The fuel economy will change depending on the behavior of the driver and the road conditions. The batteries used in the vehicle must be charged using grid electrical power

13 to achieve maximum efficiency and cost reductions. The benefit decreases if the opportunity to recharge through plug-in is unavailable.

One of the major disadvantages of a plug-in hybrid vehicle is its poor well-to- wheel efficiency compared to other hybrid vehicles. The concept of the well-to-wheel efficiency is presented next.

2.4 WELL-TO-WHEEL EFFICIENCY

The well-to-wheel efficiency is the overall efficiency of the vehicle which starts from the well where the raw material for the fuel is first extracted from the ground, and ends when the power is finally delivered to the wheels. This efficiency is the product of the well-to- tank and tank-to-wheel efficiencies. The well-to wheel efficiency is an important factor for evaluating the environmental effects of the electric, plug-in hybrid and hybrid electric vehicles. Figure 2.2 represents the processes involved in calculating well-to-wheel efficiency.

RAW MATERIAL FUEL PRODUCTION, FUEL STORAGE IN USEFUL WORK AT STAGE STORAGE AND VEHICLE TANK WHEELS DISTRIBUTION

WELL-TO-TANK TANK-TO-WHEEL WELL-TO-WHEEL Figure 2.2: Processes involved in well-to-wheel efficiency calculation

The tank-to-wheel efficiency for a conventional gasoline engine vehicle is 20 to 30%.

This is because of the low efficiency of the gasoline engine and the overall losses in the drivetrain components. In the case of hybrid electric vehicles, the engine operates at an optimal speed and torque region most of the time, which results in lower fuel consumption and higher efficiency. The tank-to-wheel efficiency of a hybrid electric vehicle is higher than that of a conventional vehicle and is estimated to be around 50%

[7]. The overall efficiency of an electric drivetrain is around 80-90%. The tank-to-wheel efficiency in plug-in hybrid vehicle is, therefore, high due to its large capacity batteries that allow the vehicle to operate in electric mode for a longer time.

The well-to-tank efficiency is calculated using “The Greenhouse Gases,

Regulated Emissions, and Energy Use in Transportation” (GREET 1.8) model. The

GREET model has been developed at Argonne National Laboratory to study the well-to- wheels energy and emissions impacts of motor vehicle technologies using various transportation fuels. The well-to-tank efficiency for the plug-in hybrid vehicle depends on the proportions of the grid energy and the gasoline energy used during the drive cycle.

The gasoline energy is used during the charge sustaining (CS) mode and the grid energy is used during the charge depleting (CD) mode of operation of the plug-in hybrid vehicle.

The share of the CS and CD modes in a drive cycle determines the well-to-tank efficiency of the plug-in hybrid vehicle.

Table 2.1 shows the well-to-tank efficiency and emission for conventional, electric and plug-in hybrid vehicles, analyzed for a typical mid-size sedan. The CG and

RFG represents conventional gasoline and reformulated gasoline vehicles. The well-to- tank (WTT) efficiency was found to be 66.5% for grid-connected hybrid vehicles (plug- 15 in hybrids) using 33% grid energy and 67% gasoline for operation. On the other hand, the

WTT efficiency for conventional and normal hybrid vehicles is 79.5%. The reason for low efficiency of plug-in HEV is due to the fact that electricity is mostly generated from conventional energy sources which have very low efficiency.

Table 2.1: Vehicle Technologies, Passenger Cars: Well-to-tank Energy Consumption and Emissions [7]

Baseline CG and Grid-Connected SI HEV: Year: 2010 Electricity RFG (SI) Gasoline and Electricity

Total Energy (Wh) 257,551 526,261 1,632,131

WTT Efficiency 79.5% 66.5% 38.0%

TTW Efficiency 21.9% 23% 48.51%

WTW Efficiency 17.41% 15.29% 18.43%

CO 2 (grams/ Million BTU) 17,495 57,024 219,704

CH 4 (grams/ Million BTU) 109.120 145.658 296.031

N2O (grams/ Million BTU) 1.152 1.535 3.111

VOC: Total (grams/ Million BTU) 27.077 25.630 19.679

CO: Total (grams/ Million BTU) 15.074 23.553 58.448

NOx: Total (grams/ Million BTU) 50.052 87.100 239.571

The greenhouse gas (CO 2, CH 4, N 2O, CO, VOC and NOx) emissions are also found to be higher in grid-connected vehicles than in conventional gasoline vehicles (Table 2.1). The

TTW efficiency for the plug-in HEV is not significantly different from the baseline vehicle since the ICE usage is still quite high. Poor efficiencies for both WTT and TTW

16 results in an overall low well-to-wheel efficiency of plug-in HEVs. A detailed well-to- wheel analysis for a series plug-in HEV is presented in Chapter 6.

2.5 PHEV OPERATING STRATEGY

A plug-in hybrid vehicle operates normally in one of two modes [8]:

Charge sustaining mode

Charge depleting mode.

In charge sustaining mode, the state of charge of the energy storage system over a driving profile is maintained within a particular band. The engine runs to maintain the state of charge and also shares power with the electric motor. In charge depleting mode, the state of charge of the energy storage system over a driving profile is allowed to deplete to a set value in the electric mode. The engine is turned ON when the state of charge falls to the set value.

The charge sustaining and charge depleting modes can be blended together to achieve maximum performance and high fuel efficiency. The blending strategy can be decided based on the road condition and the driver demand. The vehicle can initially start in the charge depleting mode to extract the maximum energy from its energy storage system and then switch to charge sustaining mode for rest of the cycle. This will give almost zero fuel consumption for distances within the battery range of the vehicle and high fuel economy for longer trips. Another strategy would be to operate the vehicle in the charge sustaining mode during periods of high power demand like hill-climbing or maximum acceleration and then switch to charge depleting mode during periods of low

17 power demands. The intended operating strategy also influences the selection of the different vehicle components.

2.6 PHEV VEHICLE COMPONENTS

The power-train of a plug-in hybrid vehicle is composed of the following primary subsystems:

Energy storage system

Electric traction motor

Electronic control circuitry

Internal combustion engine

Generator

2.6.1 ENERGY STORAGE SYSTEM: BATTERY

The energy storage system is an essential component in a plug-in hybrid vehicle. The energy storage system in a plug-in hybrid vehicle is large and can be recharged using an off-board power supply such as grid power. The energy capacity of the energy storage system may be less than that required in an electric vehicle, because of the on-board recharging capability. However, it should be larger than that of a strictly charge- sustaining hybrid vehicle so as to take advantage of the off-board recharging capability.

The selection of a suitable storage system plays an important role in designing a plug- in hybrid vehicle. There are three factors that are considered while selecting an energy storage system. They are:-

(i) Power rating

(ii) Energy capacity

(iii) Usable state of charge window.

These three factors affect the mass, cost and life of the energy storage system, as well as the fuel economy and performance of the vehicle [4]. The mass and the cost of an energy storage system are proportional to the power to energy ratio.

Usable state of charge window is related to the ratio of the usable energy capacity to the total energy capacity of the energy storage system. An energy storage system with high energy capacity is required for higher electric only range. The higher energy capacity requirement can be met by using a wide state of charge window. The state of charge window represents the energy that can be extracted out of an energy storage system during a drive cycle.

At present, two types of battery technologies are being used in developing plug-in hybrids:

Nickel-Metal Hydride (Ni MH)

Lithium-Ion (Li Ion).

Nickel-Metal Hydride (Ni MH) Battery

The Nickel-Metal hydride battery is a nickel-based battery technology introduced in the market since 1992 [1]. The battery uses nickel oxide as the positive electrode and a metal hydride as the negative electrode. The metal hydride absorbs and releases hydrogen without getting deteriorated. The electrochemical reaction is given as

MH + NiOOH M + Ni (OH) 2

The metal hydride in the negative electrode is oxidized to form metal alloy during discharge and the nickel oxyhydroxide in the positive electrode is reduced to nickel hydroxide. The present Ni MH battery technology has a nominal cell voltage of 1.2 V and can attain a specific energy of 65 Wh/kg and a specific power of 200 Wh/kg. The battery has a flat discharge profile and high recharge capability which makes it highly suitable for use in electric and hybrid vehicle applications. However, the battery technology is still suffering due to high initial cost and low cell efficiency.

Lithium-Ion (Li Ion) Battery

Lithium ion batteries were introduced in 1991 and are still in the development phase. The lithium-ion battery uses a lithiated carbon intercalation material (Li xC) for the negative electrode and a lithiated transition metal intercalation oxide (Li 1-xMyOz) as the positive electrode [1]. The electrolyte is either a solid polymer or a liquid organic solution.

The electrochemical reaction is given as

Li xC + Li 1-xMyOz C + LiM yOz

The nickel-based lithium ion battery has a nominal voltage of 4 V and a specific energy of 120 Wh/kg and a specific power of 260 Wh/kg [1]. The lithium ion batteries have high energy efficiency, and a low self-discharge rate. The battery technology is highly suitable for electric and hybrid vehicle applications.

Battery Cycle Life

The battery cycle life varies with the depth-of-discharge. The relation between the depth- of-discharge and the cycle-life [9] is given as

(M*DOD ) L= ( Lzero ) e (2.1) where L is number of discharge cycles in the life of the battery, Lzero is the number of cycles in life obtained by extrapolating cycle-life data to zero depth-of-discharge, DOD is the depth-of-discharge and M is the slope (negative) of the plot of the natural logarithm of L/L zer o with respect to DOD. The life-cycle decreases with higher depth-of-discharge.

The battery in a plug-in hybrid vehicle is typically designed to operate at around 80%

DOD [4]. Also the battery will sustain a number of deep discharge and shallow discharge cycles during operation. Assuming one deep discharge cycle per day, a typical plug-in hybrid battery will incur around 4000-5000 deep discharge cycles in a 10-15 year lifetime

[4]. The number of discharge cycles in the life of the battery will vary for different battery technologies. Figure 2.3 shows the number of discharge cycles that can be sustained by the Lead-acid, Ni MH and Li Ion batteries at different depth of discharge. It can be observed from Figure 2.3 that the number of discharge cycles sustained by a battery during its life time decreases with the depth of discharge. If a Ni MH PHEV

21 battery is to last for 4000 cycles, the energy management algorithm should limit DOD to

70%.

120

100

80 Ni MH 70 60 Li Ion Lead -acid 40 Depth of discharge (%) (%) discharge Depth of

0 0 10 100 1000 4000 100000 Cycles

Figure 2.3: Battery life cycles for different depth of discharge [10]

2.6.2 ELECTRIC TRACTION MOTOR

The electric motor converts the electrical energy from the battery pack to mechanical energy at the transaxle to propel the vehicle. The motor also converts the mechanical energy from the wheels to electrical energy during electric power generation or regenerative braking. The most important advantage of using an electric traction motor is that it can provide full torque at low speeds and its instantaneous power rating can be two or three times the rated power of the motor [2].

Induction motors and permanent magnet synchronous motors are generally used in electric and hybrid electric vehicles. Induction motors are widely used because of their

22 low cost, high reliability and maintenance-free operation. But the induction motor has very low efficiencies at light loads and a limited constant–power operation region.

Permanent magnet motors on the other hand have a wider speed region of constant power operation. Permanent magnet motors are also highly efficient and have high power densities.

Another class of motors called ‘switched reluctance motors’ are also being considered for hybrid vehicle operations. These motors have excellent fault tolerant characteristics, simple construction, low manufacturing cost and excellent speed-torque characteristics suitable for hybrid vehicle applications. But these motors suffer from acoustic noise and torque ripple during operation [11].

2.6.3 ELECTRONIC CIRCUITRY: POWER CONVERTERS

A power converter is used to supply proper voltage and current to the electric motor. The power converter is controlled by an electronic controller. The electronic controller consists of sensors, interface circuitry and processors. Figure 2.4 shows a motor drive system using a permanent magnet synchronous motor. The controller controls the operation of the motor by generating appropriate gate signals to the power converter. The control algorithm is based on stator currents and rotor position feedback information [1].

PMSM POWER BATTERY CONVERTER

Gate Current signals sensing Rotor position Communication CONTROLLER sensing Network (CAN)

Figure 2.4: Block diagram of a motor drive system 23

Power electronics circuitry is also used to supply power to the electrical accessories in the vehicle.

2.6.4 INTERNAL COMBUSTION ENGINE

The internal combustion engine (ICE) converts gasoline to mechanical energy. The ICE can be used in plug-in hybrids in two ways:

(i) To drive the wheels in parallel configuration with the electric motor.

(ii) To drive a generator for charging the battery.

An ICE has a narrow range of speed for high power operation. When the ICE is used for supplying power to the wheels, a transmission is used to match the vehicle speed with the engine speed [2].

2.6.5 GENERATOR

The generator is an electric motor mechanically coupled with the ICE to generate electric power to charge the batteries. It can also be used to start the ICE or supply assisting torque to the ICE for load leveling. Permanent magnet motors are generally used as generators in hybrid electric vehicles [11].

2.7 MARKET STATUS

At present plug-in hybrid vehicles are in a development phase. Yet, some normal hybrid vehicles have been converted into plug-in hybrids by adding higher capacity battery packs in addition to the batteries already fitted in the vehicle. Plug-in hybrid kits for commercial vehicles like Toyota Prius hybrid, Ford Escape hybrid and Mercury Mariner hybrid are now available in the market. Many hybrid vehicle manufacturers have started to design and build plug-in hybrid vehicles to diversify their product line. General Motors and Toyota have unveiled their plans for commercializing plug-in hybrid cars. Some plug-in hybrid technologies available in the market are discussed below.

Toyota Prius L5 Lithium Power

The Prius L5 Lithium Power is a plug-in hybrid vehicle with a 50-km zero-emission range. It has zero fuel consumption when driven in the city with a speed below 55 mph.

The vehicle uses a 5-kWh Lithium ion battery (L5 Lithium Power) pack in addition to the original battery. This battery can be charged from a 120-V, 15-A power outlet and takes

4-5.5 hours to charge. The battery has a weight of 72.5 kg. The vehicle was found to have a fuel economy of 100 miles per gallon for a combined city and highway drive cycle [12].

Ford Escape L12 Lithium Power

The Ford escape L12 Lithium Power is a plug-in hybrid vehicle with an 80-km zero- emission range. It has zero fuel consumption when driven in city with a speed below 55 mph. The vehicle uses a 12-kWh Lithium ion battery (L12 Lithium Power) pack in addition to the original battery. This battery can be charged from a 120-V, 15-A power

25 outlet and takes 6-12 hours to charge. The battery has a weight of 147.5 kg. The vehicle was found to have a fuel economy of 60 miles per gallon for the same combined city and highway drive cycles [12].

Chevy Volt Plug-in HEV

The Chevrolet Volt is the new plug-in hybrid concept car from General Motors. The vehicle is being designed with a 16-kWh Lithium ion battery pack to give a 40-mile zero- emission range. It will have an electric propulsion drive with an electric motor at the front wheels capable of producing a peak power of 130-140 kW. The vehicle will also use a

53-kW generator with a 3-cylinder, 1L turbocharged ICE. The ICE and the generator will be used to charge the battery pack. The vehicle is called “Range Extender” and is similar to the electric vehicle, EV1 manufactured earlier by General Motors [13].

Toyota Plug-in HEV

Toyota Motor Corporation has developed a plug-in hybrid vehicle with an 8.1-mile zero- emission range. It uses a 13-Ah capacity Nickel metal hydride (Ni MH) battery pack which can be charged from a standard 200-V power outlet. The vehicle uses an AC synchronous motor with a 50-kW peak output power. It also has a 1496-cc ICE with a maximum power output of 56 kW. The vehicle has been certified by Japan for public road tests [14].

2.8 INTELLIGENT ENERGY MANAGEMENT

Fuel economy and environmental pollution are two major factors that have led to the development of hybrid electric vehicles. Using adaptive and intelligent control techniques, energy management in hybrid vehicles can be further optimized to improve fuel efficiency and emissions in hybrid electric vehicles. Information from the driving profile, road condition and vehicle location can be used to achieve lower emission and more efficient energy use. Information about road conditions can be received in advance through modern navigational systems to predict the future speed and power requirements of the vehicle. Some intelligent energy management strategies found in the literature for hybrid electric vehicles are now discussed.

The two important issues involved in the energy management for hybrid electric vehicles are torque distribution and charge sustaining strategy. Optimization theory has been used for power distribution and energy management in hybrid vehicles. The algorithms based on these techniques distribute the power flow between the ICE and the electric motor to achieve high efficiency and low emissions [15, 16]. However, the overall approach is based on a fixed driving cycle due to the inherent characteristics of optimal control theory. It will not possible to generate an optimal control strategy for energy management in a real world scenario.

An Intelligent Energy Management Agent (IEMA) has been proposed for a parallel hybrid vehicle which takes care of the dynamic behavior of the different drive cycles [15]. The information about the vehicle state, the driver demand and the drive cycle are utilized to distribute the energy flow in an efficient way. The IEMA identifies

27 the current driving situation pattern and makes intelligent decisions with respect to the torque distribution and charge sustenance tasks and the mode of operation of the vehicle.

Figure 2.5 shows the structure of the IEMA. The engine torque command is generated based on the output of the ‘Fuzzy Torque Distributor’ and ‘Driving Situation

Identifier’. State of charge compensator generates the engine torque required for charge sustaining operation.

Roadway Roadway Fuzzy Current engine Type Type & Level Torque toque command Identifier of Congestion Distributor

Driving Incremental Situation Driving style torque Identifier Torque Engine torque command command Driving profile Driver Command Incremental engine torque command for charging Driving State of charge Information Compensator Extractor

Figure 2.5: Block diagram of the intelligent energy management architecture (IEMA)

[15]

Other intelligent energy management strategies using future vehicle parameters have been proposed for parallel hybrid electric vehicles [16]. The main idea of this concept is to develop an overall operating strategy on a trip that will minimize fuel consumption and emissions. For example, the vehicle can charge its energy storage system before it enters the city in order to run in all-electric mode. The information about the future road conditions like traffic speed and roadway gradient can be received from modern navigational equipments like OnStar (General Motor’s navigational system).

Two controllers, the instantaneous controller and the navigation controller, determine the optimal engine torque using fuzzy logic. The instantaneous controller determines the optimum torque based on the ICE efficiency maps, the battery state of charge and the total torque requirement. The navigation controller implements a predictive algorithm that varies the parameters of the instantaneous controller based on the predicted future vehicle states. The primary parameter is the torque-split ratio between the ICE and the electric motor. The block diagram of the controller is shown in Figure 2.6.

Requested Torque INSTANTANEOUS ICE Speed CONTROLLER Desired ICE Torque

Battery SOC

Traffic speed GPS NAVIGATION Signal CONTROLLER Traffic Elevation

Figure 2.6: Intelligent controller based on future vehicle states

The above strategy depends on the performance of the fuzzy logic controllers.

The fuzzy logic controller has to be trained with a large number of data in order to determine the optimum ICE operating points for a drive cycle. The sampling times, the fuzzy logic rules and the weight functions have to be tuned to achieve satisfactory results.

2.9 CONCLUSIONS

Intelligent controllers have been developed based on prediction of future vehicle parameters using modern navigational systems incorporating GPS. The driving environment such as traffic congestion, elevation on which the vehicle is driving and the driver behavior affect the fuel consumption and greenhouse gas emissions from the vehicle. These factors can be used to develop an optimal energy management and torque distribution strategy to improve fuel economy and reduce emissions.

Adaptation techniques based on the history of daily driving requirements of a particular vehicle have not yet been used in plug-in hybrid vehicles. The objective of this thesis is to present a simple intelligent and adaptive energy usage strategy that will increase the fuel efficiency of the vehicle and the life of the energy storage system. The concept calls for the development of an energy management usage strategy that will bring the battery energy to the minimum possible level at the end of a daily drive cycle and recharge it overnight from the grid.

CHAPTER III

SERIES PHEV COMPONENT SIZING AND VEHICLE CONTROL

The series hybrid electric vehicle has been a popular choice in applications where acceleration performance is not a critical issue. The drivetrain configuration is fairly simple with only the electric machine or machines powering the wheels. This drivetrain is actually an extension of an electric vehicle drivetrain that consists of an electrical transmission path from the energy storage system to the electric traction motor. The electric drivetrain has a limitation on the driving range due to the limited onboard energy storage capacity. The limitation is overcome in the series drivetrain by charging the onboard energy storage system using an engine-generator system.

Interface Circuit

Converter

Engine Generator Battery

Bi-directional power flow

Unidirectional power flow Motor Wheels

Figure 3.1: Block diagram of a series plug-in hybrid vehicle configuration

The series plug-in hybrid vehicle has a configuration similar to that of a series hybrid vehicle except that a utility interface circuit is connected to the battery terminals. The configuration of a series plug-in hybrid vehicle is shown in Figure 3.1.

In the series plug-in vehicle, the traction motor is powered by the battery or the engine-generator set. The vehicle acceleration and maximum speed are determined by the ratings of the traction motor. The zero-emission range and the fuel economy are determined by the capacity of the battery pack.

The next section focuses on the sizing of the components for a plug-in hybrid vehicle and developing a control strategy for the drivetrain. A set of constraints has been chosen for the design of a series plug-in hybrid and a series-parallel plug-in hybrid so that the performance of the two vehicles can be compared on a level situation. The design of the series plug-in HEV is presented in this chapter, while the design of the series-parallel plug-in HEV is presented in Chapter 4.

3.1 SIZING OF COMPONENTS

The major components in a plug-in hybrid vehicle are the traction motor, the battery, the engine and the generator. The determination of the power ratings of these components is an important step in vehicle design. The calculations of these parameters are done on the basis of the following chosen constraints.

1. Acceleration performance : The vehicle should be able to accelerate from 0 to

60 mph in less than 12 seconds. This constraint is selected from the PNGV

consortium goals [18]. The engine is to remain off during this period. The

minimum state of charge required for the battery at the beginning of the

acceleration is 0.8.

2. Highway driving : The vehicle should be able to cruise on the highway with

the available engine or electric power. The cruising speed is chosen to be 70

mph.

3. Zero emission vehicle range :. The daily vehicle usage data for US motorists

collected in 1995 by the National Personal Transportation Survey (NPTS)

shows that majority of average daily mileage is less than 40 miles [6]. A 40

mile zero-emission range is chosen for the design in this thesis. The vehicle

should start with an initial state of charge of 1 and the final state of charge

should be above 0.2

3.1.1 TRACTION MOTOR POWER

The power rating of the electric motor in the series plug-in hybrid vehicle is chosen to deliver the maximum required power during the acceleration from 0-60 mph. The maximum power required during this acceleration is usually sufficient to meet the power requirement at the vehicle maximum speed. The sizing is based on the assumption of using the motor at its peak torque capability until the power limit of the motor is reached.

The calculations were done using the vehicle traction force model given by

dv 2 Ftr = m + mg [sin β + C0 ] + [mgC 1 + 5.0 ρC AD A f ]v dt (3.1)

33 where

Ftr = Tractive force (N)

m= Vehicle mass (kg)

v = Vehicle velocity (m/s)

g = Gravitational acceleration (m/s 2)

β = Grade angle with respect to horizon (radians)

C0 = First co-efficient of rolling resistance (dimensionless)

2 2 C1= Second co-efficient of rolling resistance (s /m )

C AD =Aerodynamic drag coefficient (dimensionless)

ρ = Density of air = 1.16 kg/m 3

2 A f = Equivalent frontal area (m )

The vehicle drivetrain initially operates at its maximum force level; i.e.,

Ftr = Fmax ,

where Fmax is a constant force related to the maximum motor torque available. As the vehicle accelerates, the required power increases, until it reaches the maximum power rating of the electric motor. Then the vehicle drivetrain operates at its maximum power, i.e.,

Ftr = Pmax / v ,

where Pmax is the electric machine’s power rating.

The velocity profile for the acceleration cycle is simulated in MATLAB ®. The base vehicle parameters given in Table 3.1 are those for a standard mid-size sedan. A

34 vehicle mass of 1440 kg is chosen for an initial simulation prior to sizing the energy storage. Figure 3.2 shows the peak power required to meet the acceleration requirement.

Table 3.1: Series plug-in hybrid vehicle specifications

Description Parameter value

Vehicle mass 1440 kg

One driver and one passenger 160 kg

Rolling resistance coefficient (C0 ) 0.015

Aerodynamic drag coefficient (C AD ) 0.36

2 Frontal Area (Af ) 2.66 m

Tractive force, Tractive power and velocity profile Vehicle velocity,Tractive force,Tractive power vs Time 100 90

80 Power: x 1 kW 70

60 Force: x 100 N 50 40

30 26.82

20 Force(kN), Power(kW), Velocity(mps) Force(kN), Power(kW), (mps) Velocity (kW), (N), Power Force 10 Velocity (mps)

0 0 2 4 6 8 10 11 12 14 16 18 20 Time (seconds) Figure 3.2: Tractive force, power and velocity profile of the series plug-in hybrid vehicle

The peak power required to achieve the desired acceleration without drivetrain losses is

91 kW. Assuming 90% drivetrain efficiency, the minimum power required is 100 kW.

Therefore, the traction motor must have a minimum peak power capability of 100 kW.

The maximum torque was selected to be 167 Nm, which results in a propulsive force of

4850 N assuming a wheel radius of 0.3 m and a gear ratio of 10.66:1 at the lowest gear.

3.1.2 ENGINE AND GENERATOR POWER

The engine and the generator in a series hybrid drivetrain are used to supply steady state power to charge the battery and also provide sufficient power to sustain high speed operation (highway). A cruising speed of 70 mph at 1% gradient is used for calculating the power rating of the engine-generator system.

The tractive force to achieve the desired cruising speed is calculated from 3.1 as

Ftr = 937 5. N

The tractive power is given by

Ptr = Ftr ×v = 29 33. kW (3.2)

Assuming 10% losses in the drivetrain, traction motor and generator and allowing 3 kW power to recharge the batteries, the engine and generator are sized to be 42 kW each.

Figure 3.3 shows the traction power required at wheels for three different gradients with velocity as a parameter. The power required to propel the vehicle at 70 mph for a 1% gradient is 29 kW, which is well within the capability of the sized drivetrain components.

120

100

80 5% gradient

60 0% gradient 1% gradient 40

(kW) Power Traction 29.33 Engine/Gen Power (kW) Power Engine/Gen 20

0 0 50 70 100 150 Velocity (mph)

Figure 3.3: Traction power requirement at different gradient and vehicle speed

3.1.3 BATTERY SIZING

The required rating of the battery depends on the energy required for the 0-60 mph acceleration requirement and the battery-only range or ZEV range. The rate of change of energy is the instantaneous tractive power and is given by

de tr Ptr (t) = dt where e is the instantaneous tractive energy. The energy required during the tr acceleration period can be obtained by the integration of the instantaneous power equation as

12 Ptr ∆ebatt = ∫ dt 0 ηmotor ×ηdrivetrain (3.3) where (*/*- is the efficiency of the motor and -$1 /-$) is the efficiency of the drivetrain.

0.9

0.8

0.7

0.6

0.5

0.4

0.305 Battery Energy (kWh) Battery energy (kWh) (kWh) energy Battery 0.2

0.1

0 0 10 20 30 40 50 60 70 80 90 100 Velocity(mph)

Figure 3.4: Battery energy consumed for 0-60 mph acceleration demand

Assuming 90% motor efficiency and 90% drivetrain efficiency, the energy consumed from the battery when the vehicle accelerates to meet the 0-60 mph acceleration demand is shown in Figure 3.4. All the energy required for this acceleration comes from battery and the engine is OFF. The energy consumed when the vehicle reaches the speed of 60 mph is 0.305 kWh or 305 Wh.

The energy required for the ZEV range is calculated using a constant speed of 45 mph at 1% gradient and 10% drivetrain losses with a driver and a passenger [19]. The power required to sustain the desired speed is 13.67 kW. Considering a 40-mile ZEV

38 range for the plug-in hybrid vehicle, the energy required is 12.15 kWh. Hence the total energy requirement from the battery pack for acceleration and ZEV range is 12.46 kWh.

Assuming 80% DoD (depth of discharge), a 52-Ah capacity battery pack at 300 V with a peak power of 100 kW is required to meet the desired ZEV range and the acceleration requirements. The vehicle mass is 1600 kg with the selected components, assuming the body mass of a mid-size four-door sedan is 900 kg. The components selected for the series plug-in hybrid vehicle are presented in Table 3.2.

Table 3.2: Series plug-in hybrid vehicle component data

Components Rating / Description Mass (Approximate)

ICE 42 kW 100 kg

Generator 42 kW 80 kg

Traction Motor 100 kW 90 kg

Battery (Ni MH) 52 Ah, 300 V 220 kg

Other accessories Electronic Circuitry 30 kg

3.2 CONTROL STRATEGY

In a series plug-in hybrid vehicle, the engine-generator system is not mechanically coupled to the drive wheels. Thus, the speed and torque of the engine are independent of the vehicle speed. The engine is generally operated near its most efficient point in the speed-torque plane where the emissions are also not at their peak. This operation minimizes the fuel consumption of the engine. The engine is turned on and off depending

39 on the vehicle operating mode and the control strategy adopted for the drivetrain. The vehicle operating mode depends on driver behavior and driving conditions. The operating modes for the designed series plug-in hybrid vehicle are discussed in the following.

1. Electric only mode : In this mode the battery supplies power to the motor to meet

the power demand. The vehicle will be in the electric only mode as long as the

state of charge of the battery is above a certain minimum level.

2. Series mode : This mode is triggered when the battery state of charge reaches the

lower limit. The engine-generator is used to charge the battery pack. The power

flow from the engine-generator is used to propel the vehicle as well as to charge

the battery pack.

3. Regenerative braking mode : In this mode, the traction motor is used as generator

during vehicle braking. The kinetic energy from the wheels is converted to

electrical energy and is used to charge the battery pack.

The control strategy is designed to meet the driver demand, to run each component at its best operating point, to maintain the state of charge within the prescribed limits and to recapture part of the brake energy. The control rules to meet this strategy are implemented in the vehicle controller. The controller receives the commands from the driver and feedbacks from all the components in the vehicle and then selects the 40 appropriate operating mode. Figure 3.5 shows the communication of the vehicle controller with the driver and the subcomponents of the system.

Commands Driver CONTROLLER VEHICLE DRIVER DRIVEVelocity CYCLE profile Command

Feedback

Velocity feedback

Figure 3.5: Block diagram of communication network of vehicle controller

When a large power is demanded by the driver, the battery delivers the required power to the traction motor depending on its available state of charge. If the state of charge is insufficient, the engine-generator system is turned on to meet the acceleration requirements. During cruising, if the state of charge of the battery reaches a certain lower limit, then the vehicle is operated in the series mode. The upper (SOC max ) and lower

(SOC min ) limits are set in the vehicle controller based on the control strategy adopted for the driving condition. The engine-generator set delivers power until the state of charge of the battery increases to its upper (SOC max ) limit. Figure 3.6, illustrates the engine ‘ON and ‘OFF’ periods and their effect on the SOC.

SOCmax SOC SOCmin

Engine Mode ON OFF ON OFF ON OFF ON

Figure 3.6: SOC control strategy

3.3 DRIVE CYCLE SIMULATION

The series plug-in hybrid vehicle is modeled in the MATLAB/Simulink ® software platform. The vehicle subsystems are modeled using SIMPOWER and SIMDRIVELINE toolboxes in the Simulink. The 52-Ah battery is used along with the 42-kW engine- generator and the 100-kW motor. The details of the model are given in APPENDIX A.

The simulation results are presented next.

A. Acceleration Cycle

A step input for the driver demand is used for the acceleration test. Figure 3.7 shows the velocity profile of the series plug-in hybrid vehicle. The electric motor reaches the maximum power capability (Figure 3.8) in less than 10 seconds and the vehicle further accelerates with constant power operation of the electric motor.

60 Step input 50

Actual vehicle speed 40

(mph) Velocity 20

0 0 5 10 12 15 20 Time (seconds) Figure 3.7: Velocity profile of the series plug-in hybrid vehicle in the acceleration cycle

120

Motor output power

100

80 Vehicle speed

20 Power (kW) and Velocity (mph) (mph) and Velocity (kW) Power

(mph) Velocity (kW), Power (N), Force

0 0 2 4 6 8 10 12 14 16 18 20 Time(seconds)

Figure 3.8: Electric motor output power for maximum (sustained) acceleration

B. Series-mode Simulation

In order to test the series mode operation, a steady-speed test drive cycle shown in Figure

3.9 is used for simulation. In order to simulate the series-mode cycles within a feasible simulation time, the control strategy is modified to maintain the battery state of charge between 1 and 0.9. The engine is turned ‘ON’ when the state of charge goes below 0.9 and is turned OFF when it reaches 1. Figure 3.10 shows the engine ON time with respect to the battery state of charge during the series mode operation.

Speed (mph)

15 Time (seconds) Figure 3.9: Steady-speed drive(seconds) cycle for series mode testing

The power required to sustain a steady speed of 70 mph on a flat road is 25 kW.

The battery supplies the power until the state of charge goes below 0.9. The engine and the generator are then turned ON to supply power to the motor and also to charge the battery. The 42 kW power from the generator is split between the wheels and the battery.

After satisfying the power requirement at the wheels, the rest of 14 kW power goes to the battery. Figure 3.11, shows the power output from the generator, battery and the power flowing to the wheels during the series-mode operation. 100

90 SOC 80

70 60 ENGINE OFF 50 SOC (%) SOC 40 ENGINE ENGINE 30 ON ON 20

0 200 400 600 800 1000 1200 1400 Time Figure 3.10: Battery state of charge during series-mode operation 44

100 90 80 SOC

Generator output 60 power Battery discharging

20 Power Power (kW) Electric Series Mode Mode 0

Power (kW), SOCPower (%) Propulsion power at wheels Battery charging

200 400 600 800 1000 1200 1400 Time(seconds)

Figure 3.11: Component power outputs during series-mode operation

3.4 CONCLUSIONS

The designed series plug-in hybrid vehicle is suitable for driving in city and in steady- speed drive cycles as long as the traction power requirement is below 42 kW. The peak electric power of the motor is used only to meet the 0-60 mph acceleration demand. The engine-generator set serves as a range extender beyond the 40 mile range. The series plug-in hybrid simulation model and the control strategy have been validated through acceleration and series mode tests. The purpose of this vehicle design and the simulation model is to develop a tool to test the energy usage adaptation strategy for series plug-in hybrid vehicles.

A series-parallel design configuration for the plug-in hybrid vehicle that focuses on reducing component sizes is discussed in the next chapter.

CHAPTER IV

SERIES – PARALLEL PHEV COMPONENT SIZING AND VEHICLE CONTROL

The series-parallel plug-in hybrid vehicle has a more complex drivetrain architecture than a series plug-in hybrid vehicle. In a series-parallel drivetrain, the engine is mechanically coupled to the wheels through a transmission. The engine is assisted by a traction motor that is also mechanically coupled to the wheels. The transmission of power through the parallel path helps deliver the same performance as in series configuration but with a smaller engine and motor. Additionally, the series mode can be used in urban driving for better efficiency and reduced emissions.

Engine Transmission Front wheels

Converter

Generator Battery Motor Rear wheels

High Voltage Bus

Bi-directional power flow Unidirectional power flow Figure 4.1: Block diagram of the series-parallel plug-in hybrid vehicle

A series-parallel 2x2 architecture, shown in Figure 4.1, will be used for analysis in this research. In a 2x2 architecture, the engine is coupled to the front wheels through a

47 transmission and forms the mechanical transmission path. The electrical transmission path is composed of the generator, the battery and the traction motor. The generator is mechanically coupled to the engine and the traction motor is connected to the rear wheels. The traction motor also operates as a generator during vehicle braking to capture regenerative energy. The vehicle operates in the parallel or engine only mode by engaging the automatic transmission. The series mode is operated by disengaging the transmission and using the electrical path.

The next sections focus on the sizing of the components and the control strategy used for the operation of the series-parallel plug-in hybrid vehicle.

4.1 SIZING OF COMPONENTS

The major components in a series-parallel plug-in hybrid vehicle are the traction motor, the battery, the engine and the generator. The sizing of these components is done based on the same constraints used for the series plug-in hybrid configuration. The constraints are described below:

1. Acceleration performance : The vehicle should be able to accelerate from 0 to

60 mph in less than 12 seconds.

2. Highway driving : The vehicle should be able to cruise at highway speed of 70

mph with the available engine and/or electrical power in charge sustaining

mode.

3. Zero emission vehicle range: The zero-emission vehicle range is 40 miles.

The acceleration requirement is met by operating the IC engine and the electric motor at their peak torque capabilities until the power output of the latter reaches its limit.

The drivetrain is assumed to be operating with constant torque acceleration first and then with constant power acceleration. A MATLAB ® program is used for calculating the total power requirement.

A base vehicle mass of 1462 kg is considered for the initial sizing calculations.

The mass is adjusted from the series plug-in hybrid vehicle by adding the mass of the transmission and adjusting for the mass of the electric motor and the generator. The adjustment is based on the fact that the motor power can be scaled down from the series configuration. The vehicle parameters used for sizing is given in Table 4.1.

Table 4.1: Series-parallel plug-in hybrid vehicle specifications

Description Parameter Value

Vehicle mass 1462 kg

One driver and one passenger 160 kg

Rolling resistance co-efficient 0.015

Aerodynamic drag co-efficient 0.36

Frontal Area 2.66 m 2

The velocity profile, as shown in Figure 4.2, for the acceleration cycle is simulated in

MATLAB ®.

100 93

80 Power: x 1 kW

60 Force: x 100 N

26.82 20 Velocity (mps) 0 0 5 10 11 15 20 25 30 Velocity(mps),Power(kW),Tractive force(x100N) Time(seconds) Figure 4.2: Tractive force, power and velocity profile for the series-parallel plug-in HEV

The peak power required to achieve the desired acceleration without drivetrain losses has been found to be 93 kW. Assuming 90% drivetrain efficiency, the minimum power requirement is 102 kW.

The size of the traction motor can be reduced significantly in the series-parallel configuration since the engine is configured to supply power in parallel with the motor.

The 42-kW engine used in the series plug-in hybrid is used here along with a smaller traction motor. The power required for driving the series-parallel plug-in hybrid at different speeds and gradients is shown in Figure 4.3. The data assumes 90% drivetrain efficiency. It is evident from the Figure 4.3 that a 42-kW engine will be able to cruise the vehicle at 70 mph at 1% gradient.

120

100 5 % gradient

1 % gradient

Engine Power42 (kW) 0 % gradient Power (kW) Power

0 0 50 64 82 87 100 150 Velocity (mph) Velocity (mph)

Figure 4.3: Traction power requirement at different gradient and vehicle speed

4.1.1 TRACTION MOTOR POWER

The traction motor size is decided on the basis of acceleration requirement of 0-60 mph in 12 sec. The motor and engine both supply power to the wheels during acceleration.

Initial calculation shows that the motor has be rated (peak power) at 60 kW along with the 42-kW engine to achieve the desired performance. The engine is connected to the wheels through a multi gear transmission and can also deliver power to help the traction motor during acceleration. In order to obtain the actual size of the traction motor, several simulations were done. Figure 4.4 shows acceleration times for 0-60 mph with different traction motor sizes.

Acceleration time for different motor size 80 70 60 50 40 30 20 10 Traction motor power (kW) 0 8 9 10 11 12 Time (seconds) Figure 4.4: Acceleration test results with 42-kW engine and different motor ratings

The desired acceleration performance, as shown in Figure 4.4, can be achieved with a 45-

kW motor in parallel with a 42- kW engine. If the motor alone is used to provide power

to the wheels then the size of the motor has to be rated at 102 kW, as in the case of a

series vehicle. The maximum torque was selected to be 150 Nm, which results in a

propulsive force of 4750 N assuming a wheel radius of 0.3 m and a gear ratio of 10.66:1

at the lowest gear.

4.1.2 GENERATOR POWER

The generator runs in the series mode and it should be able sustain the power demand in

the city driving conditions. According to the standard urban driving cycles shown in

Table 4.2, the maximum speed in a city driving cycle is 56.7 mph.

Table 4.2: Drive cycle data

Maximum speed Average speed in Cycle in mph mph

UDDS 56.7 19.6 FTP 75 Urban 53.68 17.34 EPA NYCC 27.7 7.1

The generator in the urban driving situation should be capable of supporting this maximum vehicle speed. The power required for the series-parallel plug-in hybrid vehicle with one driver and one passenger and driving at 60 mph on a 1% gradient road is calculated using equations (3.1) and (3.2). Assuming 90% efficiency for the traction motor, generator and drivetrain, the power required is 24 kW. Assuming a minimum charging power of 3 kW, the generator is sized at 27 kW.

4.1.3 BATTERY SIZING

The size of the battery depends on the energy required for the 0-60 mph acceleration requirement and the battery-only range or ZEV range. During acceleration, the battery supplies power to the traction motor. The energy consumption from the battery during 0-

60 mph acceleration is obtained from the motor power requirement. Using Eq. 3.3 and assuming 90% motor efficiency and 90% drivetrain efficiency, the energy required during the acceleration is shown in Figure 4.5.

0.5

0.4

0.3

0.2 0.175 Battery Energy (kWh) 0.1

0 0 20 40 60 80 100 Velocity(mph)

Figure 4.5: Electrical energy consumed during acceleration

The energy consumed when the vehicle reaches the speed of 60 mph is 0.175 kWh or 175 Wh. The energy required for the ZEV range is calculated using a constant speed of 45 mph at 1% gradient and 10% drivetrain losses with a driver and a passenger

[19]. The power required to sustain the given speed is calculated using Eq. 3.2 and is found to be 13.9 kW. For a 40-mile ZEV range for the series-parallel plug-in hybrid vehicle, the energy required to cruise at 45 mph is 12.36 kWh. Hence the total energy requirement from the battery pack to meet the acceleration and ZEV range requirement is

12.53 kWh. Assuming 80% DoD (depth of discharge), a 52-Ah capacity battery pack at

300 V with a peak power of at least 45 kW is required to meet the desired ZEV range and

54 the 0-60 mph acceleration requirements. The final components selected for the series plug-in hybrid are presented in Table 4.3

Table 4.3: Series plug-in hybrid vehicle component data

Components Rating/Description Mass (Approximate)

ICE 42 kW 100 kg

Generator 27 kW 30 kg

Traction Motor 45 kW 70 kg

Transmission Automatic 90 kg

Battery (Ni MH) 52 Ah, 300 V 220 kg

Other accessories Electronic Circuitry 30 kg

4.2 CONTROL STRATEGY

The performance of the plug-in hybrid vehicle depends on the operating modes and the control strategy of the drivetrain. The operating modes and control strategy should respond to the driver demand and driving conditions.

The proposed series-parallel configuration operates in the following modes.

(A) Parallel mode :

In this mode, the motor and engine both supplies power to the wheels to meet the

peak power demand. The transmission is engaged to power the wheels directly

from the engine.

(B) Series mode – In this mode the engine/generator system supplies power to the

rear wheels through the traction motor. The state of charge is maintained

within the limits by turning the engine-generator system ‘ON’ and ‘OFF’

(shown in Figure 4.6).

SOCmax SOC SOC min

Engine Mode ON OFF ON OFF ON OFF ON

Figure 4.6: SOC control strategy

The upper (SOC max ) and lower (SOC min ) limits are set in the vehicle controller based on the control strategy adopted for the driving condition. The vehicle operates in the electric mode during the ‘OFF’ periods of the engine-generator.

wheels directly from the engine. The motor is turned OFF in this mode. This

mode is used when the vehicle speed is above 60 mph or if there is a fault in

the electrical transmission path. If the battery state of charge is low, then the

power demand from the engine is increased to supply the additional power to

charge the batteries using the generator.

(D) Regenerative braking mode : In this mode, the traction motor is used as a

generator during vehicle braking. Part of the kinetic energy from the wheels is

converted to electrical energy to charge the battery pack.

The operating modes are selected based on the algorithm in the control strategy. The control strategy is designed to use series operation most of the time, with the engine assisting during the acceleration or power demand. The selection of the parallel and series modes are based on the accelerator pedal position and the vehicle speed.

Figure 4.7 shows the algorithm for selecting different operating modes based on the driver input (accelerator pedal position) and vehicle parameter (speed). The vehicle runs in the parallel mode if the accelerator pedal input is more than 50%. The transmission is engaged and the engine supplies the extra power required for propulsion to the front wheels in addition to the electric propulsion motor which supplies power to the rear wheels.

SAMPLE Accelerator Pedal Input

YES Is Accelerator pedal > 50 %?

Is NO Vehicle speed > 60 mph?

YES

PARALLEL MODE ENGINE ONLY SERIES MODE MODE

Figure 4.7: Flowchart diagram for mode selection strategy

110 100 Accelerator 90 Parallel Mode pedal 80 70 60 50 40

30 Series Mode Velocity (mph) (mph) Velocity 20 Velocity

Accelerator pedal o/p (%) and (%) and o/p pedal Accelerator 0 0 2 4 6 8 10 12 14 16 18 20 Time (seconds) Figure 4. 8: Accelerator pedal response for

Once the vehicle reaches its desired speed, the accelerator pedal falls below 50% and the controller switches to the series or engine only mode based on the vehicle speed

(Figure 4.8). If the vehicle speed is less than 60 mph then the series mode is activated.

The transmission is disengaged and the power flows from the engine/generator through the electric transmission path. The state of charge of the battery is maintained between the limits by the engine-generator system. When the state of charge falls below the lower limit, the engine is turned ‘ON’ and the power flow from the engine-generator system is split between two paths. One flows to the traction motor to meet the vehicle load demand and the rest flows to the battery for charging. The engine is turned ‘OFF’ when the state of charge reaches its maximum limit and the motor draws power from the battery again.

This mode is efficient when the vehicle operates in a city where the vehicle speed is less than 60 mph with the engine running at a constant speed at its optimum operating point when it is ‘ON.’

If the vehicle speed is more than 60 mph or there is a fault in the electrical transmission path, then the engine only mode is selected. The power demand is met by the engine through the mechanical transmission path. It will be less efficient to use both the electrical and mechanical transmission paths simultaneously at this power level and vehicle speed. If the state of charge of the battery is less than its maximum set point, the generator is used to charge the batteries. The engine power demand is increased to supply this additional power. The battery remains fully charged during this mode and can be utilized when the vehicle enters a city or a sudden acceleration is required.

The braking mode is selected when the brake pedal input from the driver is positive. The mechanical brake command is set based on the brake pedal command by

59 the driver. The traction motor is operated as a generator to capture the regenerative braking energy. The energy is used to recharge the battery towards its full capacity.

4.3 DRIVE CYCLE SIMULATION

The series-parallel plug-in hybrid vehicle is modeled in the MATLAB/Simulink ® software platform. The codes and details of the Simulink model are given in the

APPENDIX B. The simulation results are presented next.

A. Acceleration Cycle

A step reference speed input is used for the acceleration test. As shown in Figure 4.9, the vehicle accelerates from 0-60 mph in 11 seconds. During the acceleration mode, the motor and engine operates at their peak power levels of 45 kW and 42 kW respectively

(Figure 4.10).

70 Step input 60

50 Actual vehicle speed

30 Velocity (mph) Velocity Velocity (mph)Velocity 20

0 0 2 4 6 8 10 12 Time (seconds) Figure 4.9: Velocity profile of the series-parallel plug-in hybrid vehicle in the acceleration cycle

60 Velocity

Motor output power 50

Engine output power 30

(kW) Velocity(mph)Power

(mph) Velocity (kW), Power 10

0 0 2 4 6 8 10 12 14 16 18 Time(seconds) Figure 4.10: Motor and engine power during acceleration

B. Series-mode Simulation

A steady cruising speed of 60 mph with an initial constant acceleration is used as the test cycle in simulations. The test cycle is shown in Figure 4.11.

Speed (mph)

15 Figure 4.11: Steady-speed drive cycle for series mode testing

The control strategy is configured to maintain the battery state of charge between 1 and

0.9. The engine is turned ‘ON’ whenever the state of charge goes below 0.9 and is turned

OFF when it reaches 1 as shown in Figure 4.12.

100

90 80 SOC

60 50 SOC (%) 40 ENGINE ENGINE ENGINE ENGINE 30 OFF ON OFF ON 20

0 200 400 600 800 1000 1200 1400 Time (seconds) Figure 4.12: Battery state of charge during series-mode operation

100

80 SOC

Motor output Generator output power power 40 Battery discharging

Velocity(mph)Power (kW) Velocity(mph)Power Series mode Electric (%) SOC (kW), Power Mode 0

Battery charging 200 400 600 800 1000 1200 1400 Time(seconds) Figure 4.13: Component power outputs during series mode operation 62

The power required at the wheels to sustain a steady speed of 60 mph on a level road is 17 kW. The battery supplies the power until the state of charge goes below 0.9.

The engine and generator are then turned ON to supply power to the motor and also to charge the battery. During the engine ‘ON’ period, the 27 kW power from the generator is split between the wheels and the battery. In this simulation, 18 kW goes to the wheels and 9 kW goes to the battery. Figure 4.13 shows the power output from the generator, motor and battery in the series mode.

C. Multi-mode Simulation

A two step combination of acceleration and cruising profile is chosen to test the different vehicle operating modes. The test cycle consists of two step inputs of 60 mph and 80 mph respectively at 1 second and 400 seconds. The SOC limits for the simulation are 1 (upper limit) and 0.9 (lower limit). The vehicle response to the inputs is shown in Figure 4.14.

The vehicle accelerates from 0-60 mph in 11 seconds and then from 60-80 mph in less than 10 seconds. The different operating modes are shown in Figure 4.15. The vehicle operates in the parallel mode during acceleration from 0-60 mph and 60-80 mph. The vehicle runs in the series mode when the vehicle speed is at 60 mph. After accelerating to

80 mph, the vehicle operates in engine only mode for the rest of the time.

100

80 Test cycle 70

50 Actual vehicle speed 40

Velocity (mph) Velocity

Vehicle speed (mph) (mph) speed Vehicle 30

0 0 50 100 150 200 250 300 350 400 450 500 Time (seconds)Time Figure 4.14: Velocity profile for the multi-mode test

100

80 Parallel Mode

60 Series Parallel Mode Engine output power 40 Motor output power

Velocity(mph)Power Velocity(mph)Power (kW) Series Mode Engine only Mode

0 Generator output power

0 50 100 150 200 250 300 350 400 450 500 Time(seconds)

Figure 4.15: Subcomponent output power for the multi-mode test

4.4 CONCLUSIONS

The component sizing of a series-parallel plug-in hybrid vehicle has been presented. A control strategy has also been developed. The simulation results of the vehicle, implemented in MATLAB/Simulink ®, verify the design and validate the control strategy.

The purpose of this vehicle design and simulation model is to develop a tool to test the energy usage adaptation strategy for a series-parallel plug-in hybrid vehicle. The next chapter presents the newly developed strategy for both the series and series-parallel plug- in hybrid vehicles.

CHAPTER V

PHEV ENERGY USAGE ADAPTATION

The energy storage system used in a plug-in hybrid vehicle has a large capacity and needs to be charged from the grid electricity to obtain the maximum benefits of cost per mile and fuel economy. In order to take the advantage of the available grid power, the control strategy in the vehicle should ensure that the state of charge is at its minimum level at the end of the day. The energy usage adaptation strategy presented in this chapter is intended to leave the battery as fully discharged as possible, given the uncertainty of the requirements of daily usage. The strategy adapts to the driver usage of the vehicle, which is represented in terms of the daily energy usage pattern for a certain number of days. In addition to decreasing the cost per mile of operation of the plug-in hybrid vehicle, the strategy helps to extend the battery life by allowing at most one deep discharge cycle per day.

The adaptation strategy will be compared with a baseline strategy termed as ‘no- adaptation strategy’ where the battery state of charge is cycled each time through its maximum possible range of depth of discharge. In the no-adaptation strategy the battery is allowed to deplete to the minimum SOC, after which the engine/generator is turned

‘ON’ to re-charge to its full capacity. The no-adaptation strategy operates without regard to the drive cycle history. At the end of the drive cycle, the battery may be fully charged, fully discharged or anywhere in between depending on the exact length of the drive cycle

66 and the period of the charge/discharge cycles in a day’s operation. Figure 5.1 shows the state of charge profile for the series plug-in hybrid configuration during a HWFET cycle.

A 5-Ah battery is used for simulation. It happens in this case that the battery is almost fully discharged at the end of the drive cycle. However, it uses almost two deep discharge cycles to complete the drive cycle.

SOC vs time: Day:12 100

(%) SOC 50

(%) charge State of 30 Charge depleting 20 mode

10 0 100 200 300 400 500 600 700 800 Time Time (seconds) Figure 5.1: State of charge profile in the HWFET cycle The state of charge profile for a longer drive cycle consisting of two HWFET drive cycles concatenated is shown in Figure 5.2. In this case, the battery happens to be charged to nearly 100% at the end of the cycle. This is not an advantageous utilization of the plug-in hybrid because the onboard energy instead of the grid power is used to charge the battery. The proposed adaptation algorithm adjusts the charging and discharging profile of the battery such that the battery undergoes only one deep discharge cycle and is recharged using mostly grid electricity rather than the engine.

SOC vs time: Day:12 100

50 (%) SOC 40

10 0 200 400 600 800 1000 1200 1400 TimeTime (seconds)

Figure 5.2: State of charge profile in HWFET_2 cycles

5.1 ENERGY USAGE ADAPTATION

In the Energy Usage Adaptation (EUA) strategy, the charging and discharging profile of the battery is controlled on the basis of daily electrical energy consumption at the wheels.

The objective is to completely exhaust the battery energy by the end of the daily drive cycle to be subsequently replenished to its full capacity using grid electricity in the evening. This is possible by comparing the predicted drive cycle energy requirement with the available energy of the battery. The result of this comparison is used to control the charge and discharge limits of the battery. The EUA strategy consists of two algorithms: a prediction algorithm that predicts the drive-cycle energy requirement from a historical data set and an energy management algorithm that changes the state of charge limits of the battery based on the prediction.

5.1.1 PREDICTION ALGORITHM

A typical citizen might drive the same route everyday for the daily commute to his or her workplace. Although the daily drive cycle would vary slightly depending on the traffic and small deviations by the driver, it could be assumed that the daily energy requirement at the wheels would be within a certain percentage of the average daily usage. The vehicle supervisory controller should be capable of calculating the energy used each day and storing the daily values in its memory. The function of the prediction algorithm is to predict the energy usage for a given day using these stored data from previous days based on a set of rules. The following assumptions have been used in deriving the rules:

• If the driver is driving the same route for the previous n days, then he is most

likely to drive the same route on the (n+1) th day.

• If the driver establishes a new driving habit and drives the new route for at least

four days (at least 50% of the week is considered), then he or she is most likely to

drive the new route the next day. A new route driven for less than four days is

considered as an ‘exception.’

An initial data set consisting of daily energy usage by the driver over a period of several days is provided at the beginning and these data are replaced by the new data collected at the end of each day on a FIFO (First In First Out) basis. The data record of n days’ energy usage is used for predicting the energy that will be used the next day. The important parameters used in the prediction algorithm are:

ͬ)_) 2 = actual energy usage in the last day

ͬ1"_) = average of the n stored values of daily energy usage.

∆ $'4 = a small energy usage variation band

∆( ) = a large energy usage variation band.

If {ͬͥ, ͬͦ, . . ., ͬ)} is the data set of actual energy used for the n days prior to last day (n

≥ 3), then

ͥ ͬ = ʚ ʛ ∑) ͬ . 1"_) ) $Ͱͥ $

In real life a driver does not follow a fixed drive cycle every day in exactly the same way even though he or she may be driving the same route. The variation in the daily drive cycle is related to the variation in daily energy usage. This variation is accounted for by the large variation band ∆( ) and is given by

∆( ) = (% variation) × ͬ)_) 2 .

A maximum variation of 10% in energy usage of a drive cycle will be considered. ∆ $'4 accounts for the variation in daily energy usage within a small variation range. ∆ $'4 can also be used for compiling statistical data to understand the driver’s daily usage pattern over a much longer period of time. ∆ $'4 is calculated as

∆ $'4 = 0.1 × ͬ1"_)

Two prediction algorithms are proposed. One based on the moving average of the daily energy usage for n days is proposed first. The second one excludes any exceptions from the moving average and handles the exceptions separately. Each algorithm has its

70 advantages and disadvantages depending on the pattern of daily energy usage. The differences in the algorithms and their effectiveness in making predictions depending on the pattern of daily energy usage will be studied through simulation after the description of the two prediction algorithms.

A. Prediction Algorithm Based on Moving Average

The moving average prediction algorithm stores the energy usage data of the past certain number of days ( n days) in a memory location. Given the history of energy usages for the past n days, the prediction of the energy usage for the next day is based on the following rules:

Rule 1

If the deviation of the actual energy used in the last day from the average (of n days) is small, say within ± ∆ $'4 , then the energy usage of the last day is used as the predicted value. The energy usage on the last day is stored in the memory using FIFO rule.

Rule: If | ͬ)_) 2 - ͬ1"_) | ≤ ∆ $'4 , then the predicted energy usage is ͬ)_) 2 .

Rule 2

If the deviation of the actual energy used in the last day from the average (of n days) is not within ± ∆ $'4 , then this data (energy usage in last day) is compared with the energy usage data from the previous three days (n, n-1 and n-2). If the deviation of ͬ)_) 2 from the energy used in the previous is three days within a larger band, ±∆( ) , then the

71 predicted energy usage is the average of the energy usage data from the last four days.

The energy usage on the last day is stored in the memory using FIFO rule.

Rule: If |ͬ)_) 2 - ͬ1"_) | > ∆ $'4 and if | ͬ)_) 2 - ͬ)ͯ$ | ≤ ∆( ) for all i=0, 1 and 2,

) then predicted energy usage is (1/4) ʚ∑$Ͱ)ͯͦ ͬ$ ƍ ͬ)_) 2 ʛ

Rule 3

±∆( ) , then the predicted energy usage is the average of all the previous n energy usage data including the last one ( ͬ)_) 2 ʛ . The energy usage on the last day is stored in the memory using FIFO rule before calculating the new average.

Rule: If | ͬ)_) 2 - ͬ1"_) | > ∆ $'4 and if | ͬ)_) 2 - ͬ)ͯ$ | > ∆( ) for any i=1, 2 and

1 ) 3, then the predicted energy usage is ʚ Ɵ͢ʛ ∑$Ͱͥ ͬ$ where ͬ) = ͬ)_) 2 .

The flowchart for the moving average based prediction algorithm is shown in Figure

5.3.

Save data from Energy Management

algorithm ( ͬ)_) 2 )

Extract last ‘n’ days' data in the data file (data.m)

1. COMPUTE ͬ1"_), = average of ‘n’ days data. ͬ1"_) 2. COMPUTE d_new; d_new = ͬ)_) 2 - ͬ1"_) d_new = deviation from average

x(1) = x(2), x(2) = x(3), NO YES | d _new | ≤ ∆ $'4 ? . .

x (n) = ͬ)_) 2

NO | ͬ)_) 2 – x(n-1) | ≤ Predicted energy: ∆( ) ? Epred = ͬ)_) 2

YES

x(1) = x(2), x(2) = x(3), To Energy . Predicted energy : ≤ NO Management | ͬ)_) 2 – x(n-2) | . E = ͬ pred 1" _) 2 Algorithm ∆( ) ? x(n) = ͬ)_) 2

YES COMPUTE ͬ1" _) 2 ͬ = average of new ‘n’ data 1" _) 2 Predicted energy: Epred = ͬ1" _ͨ | – x(n-3) | ≤ NO ͬ)_) 2 ∆( ) ? x(1) = x(2), x(2) = x(3), YES . ͬ1" _ͨ= ͬ)_) 2 + x(n)+x(n-1)+x(n-2) . 4 x(n) = ͬ)_) 2

Figure 5.3: Flowchart diagram for moving average based prediction algorithm

B. Prediction Algorithm Based on Exception

In the prediction algorithm based on exception, if the actual energy used on a day is completely different from the actual energy used on the previous days then this day is considered as an ‘exception’ and is not used for prediction until it has repeated for four consecutive days. The energy usage data for the exception day is stored in a separate memory known as the ‘exception memory’ which can store a maximum of three energy usage data points. Consider {ͭͥ, ͭͦ, ͭͧ} as the data set of actual energy used in case of exceptions where ͭͧ is the actual energy used on the last exception day. The data are replaced on a FIFO (First In First Out) basis when the memory is full.

The prediction for the new day is based on the following rules:

Rule 1

If the deviation of the actual energy used in the last day from the average (of n days) is within ± ∆ $'4 , then the energy usage of the last day is used as the predicted value. The actual energy usage data from last day, ͬ)_) 2 , is stored in the main memory using FIFO rule.

Rule: If | ͬ)_) 2 - ͬ1" | ≤ ∆ $'4 , then the predicted energy usage is ͬ)_) 2 .

Rule 2

If the deviation of the actual energy used in the last day from the average (of n days) is not within ± ∆ $'4 , then this data (energy usage in last day) is compared with the energy usage data from the previous three days (n, n-1 and n-2). If the deviation is within the larger band, ±∆( ) , then the predicted energy is the average of the energy usage data

74 from the previous three days and the last day. The actual energy usage data from last day, ÎÄ_Ä»Í is stored in the main memory using FIFO rule.

Rule: If |ͬ)_) 2 - ͬ1"_) | > ∆ $'4 and if | ͬ) - ͬ)ͯ$ | ≤ ∆( ) for all i=0, 1 and 2,

) then the predicted energy usage is (1/4) ʚ∑$Ͱ)ͯͦ ͬ$ ƍ ͬ)_) 2ʛ.

Rule 3

±∆( ) , then the energy usage data ͬ)_) 2 is considered as an ‘exception.’ If there were exceptions on the last three consecutive days then ͬ)_) 2 is compared with the previous exception days ( ͭͥ, ͭͦ,ͭͧ) and if the deviation is within ± ∆( ) , then the predicted energy usage is the average of all these four days. The actual energy usage data from last day, ͬ)_) 2 and all the data in the exception memory, ͭͥ, ͭͦ and ͭͧ are stored in the main memory using FIFO rule.

Rule: If | ͬ)_) 2 - ͬ1"_) | > ∆ $'4 and if | ͬ) - ͬ)ͯ$ | > ∆( ) for any i=0, 1 and 2, and if | ͬ)_) 2 - ͭ$ | ≤ ∆( ) for all i=1, 2 and 3, then the predicted energy is

ͧ (1/4) ʚ∑$Ͱͥ ͭ$ ƍ ͬ)_) 2ʛ.

Rule 4

If the actual energy used on the last day is an exception according to Rule 3, and if there were consecutive exceptions on the last three days then this data is compared with the previous exception days ( ͭͥ, ͭͦ,ͭͧ). If the deviation is not within ± ∆( ) , then the 75 predicted energy is the average of all the previous n days not considering the exception days. The actual energy usage data from last day, ͬ)_) 2 is stored in the exception memory data set using FIFO rule.

Rule: If | ͬ)_) 2 - ͬ1"_) | > ∆ $'4 and if | ͬ) - ͬ)ͯ$ | > ∆( ) for any i=0, 1 and 2, and if | ͬ)_) 2 - ͭ$ | > ∆( ) for any i=1, 2 and 3, then the predicted energy is ͬ1"_).

Rule 5

If the actual energy used on last day is an exception according to Rule 3 and there were no consecutive exceptions for the last three days, then the predicted energy is the average of all the previous n days. The actual energy usage data from last day, ͬ)_) 2 is stored in the exception memory.

Rule: If, ͬ)_) 2 | - ͬ1"_) | > ∆ $'4 and if | ͬ) - ͬ)ͯ$ | > ∆( ) for any i=0, 1 and 2, and if there were no consecutive exceptions on the last three days, then the predicted energy is ͬ1"_).

Rule 6

If the number of exceptions in daily drive cycles becomes twice more than the normal daily drive cycle within a period of 30 days, the exception data set is stored in the n day’s data set using FIFO rule.

The flowchart for the exception based prediction algorithm is shown in Figure

5.4.

Save data from Energy Management

algorithm ( ͬ)_) 2 )

Extract last ‘n’ days data in the data file (data.m)

COMPUTE ͬ1"_) ; ͬ1"_) = average of ‘n’ previous days data.

COMPUTE d_new; d_new = ͬ)_) 2 - ͬ1" _) = deviation from average

NO YES | d_new | ≤ ∆ $'4 Reset ‘i’ ? x(1) = x(2), Increment ‘i’ x(2) = x(3), . . Store ͬ)_) 2 in exception array NO i =3 NO x(n) = ͬ)_) 2 | ͬ)_) 2 – x(n ) | ≤ y (k); k=1:3 ? ∆( ) ? y(1) = y(2), y(2) = y(3), NO k =3 YES YES ? Predicted energy: y(3)= ͬ)_ ) 2 Epred = ͬ)_) 2 YES |ͬ)_) 2 – y (3) | | ) | ≤ NO NO ͬ)_) 2 – x(n-1 ≤ ∆( ) ? NO xx + yy Increment = 30 ? ∆( ) ? xx YES YES

YES NO yy > 2xx ? |ͬ)_) 2 – y (2) | NO To Energy ≤ ∆( ) ? YES Management NO | – x(n-2) | ≤ Algorithm ͬ)_) 2 x(n) = ͬ)_) 2 , ∆( ) ? x(n-1) = y(3), YES x(n-2) = y(2), x(n-3) = y(1) YES Increment ‘xx’

Increment ‘yy’ NO Predicted |ͬ)_) 2 – y (1) | energy: Epred ≤ ∆( ) ? = ͬ1" _) Predicted energy: Reset ‘i’ Epred = ͬ1" _ͨ YES x(n) = ͬ)_) 2 , x(n-1) = y(3), ͬ1" _ͨ= ͬ)_) 2 + y (1) + y (2) + y (3) x(n-2) = y(2), 4 x(n-3) = y(1)

x(1) = x(2), x(2) = x(3), ͬ1" _ͨ= ͬ)_) 2 + x (n) + x (n-1) + x (n-2) . 4 . x(n) = ͬ)_) 2

Figure 5.4: Flowchart diagram for exception based prediction algorithm 77

Drive Cycles and Variables Used in Simulation

The drive cycles and variables chosen for simulating the algorithms are:

The standard UDDS and HWFET cycles have been used for simulating the daily drive cycles. The Urban Dynamometer Driving Schedule or UDDS is a drive cycle that simulates an urban driving route of 7.5 mile. The cycle runs for 1369 seconds with frequent stops and has an average speed of 19.6 mph. The Highway Fuel Economy Test or HWFET simulates the highway driving cycle. The cycle runs a distance of 10.26 miles in 765 seconds and has an average speed of 48.3 mph.

The ∆( ) used in the simulation is based on a maximum deviation of ±10% of the actual energy usages in the drive cycles being followed. A 10% variation in the

UDDS and HWFET cycles is equivalent to a deviation of 166 Wh and 254 Wh, respectively, in energy usage. In simulations, a fixed ∆( ) of 250 Wh has been assumed for simplicity. The value of ∆ $'4 is 10% of ∆( ) . Figure 5.5 and Figure 5.6, shows the variations of the UDDS and HWFET cycles from 0 and Ə 10%.

At least one week of data is required for prediction. The number of daily energy usage data that will be stored in memory for the simulation is eleven, i.e. n=11.

The simulation of the prediction algorithms based on moving average and exception is presented next.

70 ε = +10% 60 50 ε = -10% 40 ε = 0

30 (mph)

Velocity 20

0 0 100 200 300 400 500 600 700 Time (seconds)

Figure 5.5: HWFET cycles with variation

70 ε = +10% 60

50 ε = 0% 40 ε = -10% 30

(mph)Velocity 20

0 1 101 201 301 401 501 601 701 801 901 1001 1101 1201 1301 Figure 5.6: UDDS cycles with variation

Prediction Algorithm Test

The prediction algorithms based on moving average and exception concepts are tested with a set of different daily drive cycles to study the effectiveness of the algorithms to adapt to sudden and gradual. The tests results are presented below.

A. Test 1

Maximum data storage ( n): 11

Initial energy usage data set (kWh): [1 1 1 1 1 1 1 1 1 1 1]

Simulated actual energy used for next eleven days (kWh): [1 1 1 1 1 10 10 1 1 1 1]

Usage and prediction graph Usage and prediction graph 10 10 Actual Energy Actual Energy 9 9

8 8

7 7

6 6 Predicted 5 5 Energy 4 Predicted 4 Predicted Energy 3 Energy Energy usage in kWhr (x) 3 Energy usage in kWhr (x) 2 2 1 1 12 13 14 15 16 17 18 19 20 21 22 12 13 14 15 17 18 19 20 21 22 0 16 1 0 1 LastDays 11 days Last Days11 days Figure 5.7: Actual and predicted energy Figure 5.8: Actual and predicted energy usage graph for eleven days using usage graph for eleven days using prediction algorithm based on prediction algorithm based on

moving average, Test 1. exception, Test 1.

Figure 5.7 and Figure 5.8 show the actual and the predicted energy for eleven days with the moving average and exception based algorithms. The vehicle follows the same daily drive cycle (actual energy usage: 1 kWh) for all the days except on Day 17 and Day 18 where it goes for a longer drive (actual energy usage: 10 kWh). The algorithm based on

80 moving average considers the actual driven energy on these two days for predicting energy on Day 19 through Day 22. As a result, when the vehicle switches back to its normal drive cycle from Day 19, the predicted energy is the average of all the previous eleven days (last four days are not same). The predicted energy is much higher than the actual energy from Day 19 to Day 22. If this pattern of daily drive cycles is repeated there will be an error in the energy prediction for most of the days. The error will result in under utilization of the battery.

The prediction algorithm based on exception on the other hand identifies Day 17 and Day 18 as ‘exception’ days and excludes them for predicting the energy usages for

Day 19 to Day 22. Consequently, the prediction is accurate for all the eleven days except on the two days where predicted energy is much lower than the actual energy used. This is advantageous since the prediction error is low for 80% of the days and the battery is efficiently utilized.

B. Test 2

Maximum data storage ( n): 11

Initial energy usage data set (kWh): [1 1 1 1 1 1 1 1 1 1 1]

Simulated actual energy used for next eleven days (kWh): [1 1 10 10 10 10 10 1 1 1 1]

Figure 5.9 and Figure 5.10 show the actual and the predicted energy for eleven days with the moving average and exception based algorithms. The vehicle follows a similar daily drive cycle (actual energy usage: 1 kWh) for all the days except from Day 14 to Day 18 where it uses a longer daily drive cycle for five consecutive days (actual energy usage: 10 kWh).

Since, the actual energy used from Day 14 to Day 17 are always different from the previous four days, the average energy usage (for eleven days) is used by the moving average based algorithm for prediction on all these days. On Day 18 since the vehicle has already driven the same route for four consecutive days, the algorithm adapts to the new route. It can be observed from Figure 5.9 that the error between the predicted and the actual energy decreases as the vehicle continues driving the same route.

On the other hand, prediction algorithm based on exception identifies Day 14 to

Day 17 as exception and excludes them during prediction on these days. As a result the average energy usage of previous eleven days (excluding Day 14 to Day 17) is continuously predicted until Day 18 when the algorithm adapts to the new drive cycle.

Again, when the driver switches to a different route from Day 19 to Day 22, the algorithm takes them as exception and predicts the average energy usage of previous eleven days (excluding Day 19 to Day 21). The error in prediction stays high all these days causing inefficient utilization of the battery. The prediction algorithm based on moving average proves to be marginally better here as the error is gradually reduced and battery is better utilized as the vehicle uses the same drive cycle for longer days.

Usage and prediction graph Usage and prediction graph 12 10 Actual Energy Actual Energy 9 10 8 Predicted Predicted Energy 7 8 Energy 6

6 5 Predicted Predicted 4 4 Energy 3 Energy usage in Energy usage kWhr (x) Energy usage in kWhr (x) Energy

2 2 1 12 13 14 15 16 17 18 19 20 21 22 12 13 14 15 16 17 18 19 20 21 22 0 0 1 1 LastDays 11 days Last 11 days Figure 5.9: Actual and predicted energy Figure 5.10: Actual and predicted energy usage graph for eleven days using usage graph for eleven days using

prediction algorithm based on prediction algorithm based moving average, Test 2. on exception, Test 2.

C. Test 3

Maximum data storage ( n): 11

Initial energy usage data set (kWh): [1 1 1 1 1 1 1 1 1 1 1]

Simulated actual energy used for next eleven days (kWh): [1 2 3 4 5 6 5 4 3 2 1]

Figure 5.11 and Figure 5.12 show the actual and the predicted energy for eleven days

with the moving average and exception based algorithms. The driver uses different daily

drive cycles each day.

Since the actual energy usages are always different from the previous four days

from Day 13 to Day 21, the moving average based prediction algorithm predicts the

average energy usage of the previous eleven days. On the other hand, the exception based

prediction algorithm identifies Day 13 to Day 21 as exceptions and excludes those data

during prediction for these days.

Usage and prediction graph Usage and prediction graph 6 6

5 5 Predicted

4 Energy 4 Predicted Energy 3 3

13 15 18 22 2 12 14 16 17 19 20 21 2 Energy usage in kWhr (x) Energy usage in kWhr (x)

1 1 12 13 14 15 16 17 18 19 20 21 22 Actual Energy Actual Energy 0 0 1 1 Days LastDays 11 days Last 11 days Figure 5.11: Actual and predicted energy Figure 5.12: Actual and predicted energy

usage graph for eleven days using usage graph for eleven days using prediction algorithm based on prediction algorithm based on exception, Test 3. exception, Test 3.

Table 5.1 and Table 5.2 show the data stored in the memory on each day for moving

average and exception based algorithm, respectively. The data used for prediction are

represented in bold format.

Table 5.1: Main memory data for moving average based prediction Day Day Day Day Day Day Day Day Day Day Day Initial Data 12 13 14 15 16 17 18 19 20 21 22 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 2 3 1 1 1 1 1 1 1 1 1 2 3 4 1 1 1 1 1 1 1 1 2 3 4 5 1 1 1 1 1 1 1 2 3 4 5 6 1 1 1 1 1 1 2 3 4 5 6 5 1 1 1 1 1 2 3 4 5 6 5 4 1 1 1 1 2 3 4 5 6 5 4 3

M A M NI O M E M RY 1 1 1 2 3 4 5 6 5 4 3 2 1 1 2 3 4 5 6 5 4 3 2 1 Predicted 1 1 1.09 1.27 1.55 1.91 2.36 2.73 3 3.18 3.27

Table 5.2: Main and Exception memory data for exception algorithm

Initial Day Day Day Day Day Day Day Day Day Day Day Data 12 13 14 15 16 17 18 19 20 21 22 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

M A M NI O M E M RY 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2 2 2 3 4 5 6 5 4 4 0 0 0 3 3 4 5 6 5 4 3 3

MEMORY MEMORY 0 0 0 0 4 5 6 5 4 3 2 2 EXCEPTION EXCEPTION Predicted 1 1 1 1 1 1 1 1 1 1 1

Table 5.3: Actual energy and predicted energy from Day 12 to Day 22 Predicted Energy (kWh) Actual (Predicted - Actual) Energy (kWh) Days Moving Average Exception Energy(kWh) Moving Average Exception 12 1 1 1 0 0 13 1 1 2 -1 -1 14 1.09 1 3 -1.91 -2 15 1.27 1 4 -2.73 -3 16 1.55 1 5 -3.45 -4 17 1.91 1 6 -4.09 -5 18 2.36 1 5 -2.64 -4 19 2.73 1 4 -1.27 -3 20 3 1 3 0 -2 21 3.18 1 2 1.18 -1 22 3.27 1 1 2.27 0

Table 5.3 shows the comparison between the moving average and the exception based prediction algorithms. If the actual energy usage is higher than the predicted energy and the available battery energy is less than the actual energy usage, then the battery SOC cycles in the lower SOC band. If the predicted energy is higher than the actual energy usage and the available battery energy is less than the actual energy usage, the battery

SOC cycles in the upper SOC band. In case of the moving average based algorithm, the

SOC cycles in the lower band from Day 13 to Day 19 and in the upper band for Day 21 and Day 22. The algorithm predicts the correct energy usage on Day 20. In case of the exception based algorithm, the battery SOC always cycles in the lower band from Day 13 to Day 21. Also, it can be observed that the error in the prediction is much higher in exception algorithm compared to the moving average algorithm from Day 13 to Day 19.

Hence, in this example, exception algorithm causes the battery to cycle more in the lower band than the moving average algorithm.

The moving average based algorithm is found to be suitable for gradual and random changes in the drive cycle pattern while the exception based algorithm is better suited for sudden changes in driving pattern. The advantage and disadvantage of both concepts also depend on the scale of variation between different drive cycles.

The MATLAB code for the prediction algorithm is given in APPENDIX C. The predicted energy is used by the energy management algorithm to modify the state of charge limits during the drive cycle. The energy management algorithm is presented next.

5.1.2 ENERGY MANAGEMENT ALGORITHM

The energy management algorithm depends on the energy predicted from the prediction algorithm. The predicted energy ʚ̿+- ʛ is the total energy that the algorithm assumes the vehicle would require to drive on the day. The actual energy delivered ʚ̿0. ʛ at the wheels is calculated as the vehicle is being driven on the day. The energy required

ʚ̿- ,0$- ʛ to complete the daily commute cycle is also calculated by subtracting ̿0. from ̿+- . The energy required ʚ̿- ,0$- ʛ is now compared with the available energy

ʚ̿// -4 ʛ from the battery to set the state of charge limits of the energy storage system.

The proposed algorithm for energy management is shown in the flowchart of Figure 5.13.

The algorithm is based on the following rules:

Rule 1: ̿- ,0$- ƙ ̿// -4

The battery energy is enough to propel the vehicle for the whole drive cycle without using the engine/generator. The vehicle will run in the electric mode and will deplete the battery energy at the end of the drive cycle (Figure 5.14). This allows the battery to be recharged with the lower cost grid electricity at the end of the day. In Figure 5.14 the battery state of charge is set at its lower limit. This limit would depend on the type of battery technology used. This mode of battery usage is also known as the charge depleting mode operation. The operation in this mode results in zero vehicle emission and high tank-to-wheel efficiency.

Compute energy required for the day using prediction algorithm

ʚ̿ ʛ +- YES

End of day? NO Compute energy used at wheels

ʚ̿0. ʛ

Compute energy required to

complete the drive cycle

̿- ,0$- = ̿+- - ̿0.

NO SET SOCmax: 0.5 ̿- ,0$- > 0? SET SOCmin: 0.3 SET lower SOC YES limit: 0.2 B YES YES End of day? ̿- ,0$- ƙ ̿ // -4 ?

NO NO Count the number of low band SOC cycles. SET SOCmax: 1 (N) SET SOCmin: 0.9

INCREASE SOCmax by 0.1 NO YES SOCmax=1 ͈ > 7? INCREASE SOCmin by 0.1 SOCmin=0.9 ? YES Reset N NO

B Reset N B

Figure 5.13: Flowchart for the energy management technique.

SOC max

State of charge charge ofState

SOC min

Time

Figure 5.14: State of charge profile for ̿- ,0$- ƙ ̿// -4

Rule 2 : ̿- ,0$- Ƙ ̿// -4

The energy required from the battery is not enough to complete the drive cycle for the day. So, the engine/generator system is used to maintain the state of charge within a narrow band until the required energy ʚ̿- ,0$- ʛ computed during driving is equal to or less than the energy available from the battery (Figure 5.15). This state of charge window is also known as shallow cycling of the battery which refers to the charge/discharge cycles within top 20% or less of the battery capacity. SOC max

SOC shallow_min

charge ofState

SOC min

Time Figure 5.15: State of charge profile for ̿- ,0$- > ̿// -4 As an example, the lower state of charge limit, SOCshallow_min , of the battery may be set at

0.9 and the engine/generator can be used to maintain the state of charge between 1 and

0.9. This operation may also be termed as charge sustaining mode of operation with a

SOC window of 10%. When the energy required ( ̿- ,0$- ) reaches battery energy level

(̿// -4 ), the battery state of charge is set at the lower limit (SOC min ) and the vehicle goes into all-electric mode or charge depletion mode of operation (Figure 5.15).

Rule 3: ̿- ,0$- Ɨ 0

Rule 1 and Rule 2 are applicable when the predicted energy for the daily drive cycle is within ∆ $'4 or ∆( ) . In other words, the daily drive cycle has not changed significantly and the driver is still driving on the same route as he has done for the last four days. It may be possible that one day the driver suddenly changes his usual route of driving and goes for a longer trip. The predicted energy in this case will be the less than the actual required energy for the day. In such a case, the calculated energy required

ʚ̿- ,0$- ʛ becomes negative when the actual used energy during the drive cycle becomes more than the predicted energy. In this situation, the state of charge of the battery is maintained at lower end of the SOC band (Figure 5.16). This allows the battery to be charged from the grid if the new drive cycle is only slightly longer than was anticipated. The band is gradually increased towards the upper SOC limit anticipating a long drive cycle.

SOC max SOC shallow_min

̿- ,0$- < 0 State of SOC shallow_max

charge (0, 1)

SOC min

Time Figure 5.16: State of charge profile using 90

In a series hybrid configuration, if 100% SOC limit is reached, the vehicle runs in the charge sustaining mode with a 10% shallow cycle for the longer than usual daily drive cycle. In the case of series-parallel plug-in hybrid vehicle, if the battery is charged to

100% SOC then the engine only mode is used for driving for the longer than usual daily drive cycle.

5.2 RESULTS FROM ENERGY USAGE ADAPTATION STRATEGY

The energy usage adaptation algorithm has been implemented in the

MATLAB/Simulink ® software platform. The Simulink model and the MATLAB program are provided in APPENDIX D.

The series plug-in hybrid is used with a 5-Ah battery to test the energy usage adaptation algorithm. An initial data set (shown in Table 5.4) representing the energy usage in the previous eleven days is provided to the prediction algorithm. The vehicle is then simulated through eleven drive cycles to represent eleven days of driving. The drive cycles consists of two different types taking into consideration that each type of the daily drive cycles will not be exactly the same in two different days due to traffic and driver behavior. The two drive cycles chosen for simulations are UDDS and HWFET drive cycles. The variations in the daily energy usage are approximated by a scaling factor on the drive cycle. For example, a scaling factor of 1.001 represents a variation of 0.1% of the drive cycle’s speeds and distances. The cycles and variations from Day 12 to Day 22 are shown in Table 5.5. The prediction algorithm used in the simulations to predict the energy for a new day is based on the moving average concept.

The input data summarized in Table 5.5 represents a pattern where the driver

follows the same route for five days (Day 13 to Day 17), and then switches to a new route

which is then continued for the remaining days.

Table 5.4: Initial input data for last eleven days

Days Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Day 10 Day 11

Energy 2.28 2.2758 2.29 2.2758 2.3 2.2 2.2758 2.278 2.27 2.18 2.22 (kWh)

Table 5.5: Input data for the next eleven days

Days Day 12 Day 13 Day 14 Day 15 Day 16 Day 17 Day 18 Day 19 Day 20 Day 21 Day 22

UDDS HWFET HWFET HWFET HWFET HWFET UDDS UDDS UDDS UDDS UDDS Cycle x 1 x 1 x 1.001 x 1.002 x 1.004 x 1.003 x 1 x1.001 x1.003 x1.005 x1.002 Energy 1.6684 2.5405 2.5459 2.5512 2.5619 2.5565 1.6684 1.6915 1.6953 1.7014 1.6922 (kWh)

Figure 5.17 shows the initial data used for predicting the energy usage on Day 12 and the

predicted energy for Day 12. The state of charge profile on Day 12 is shown in Figure

5.18. The predicted energy requirement is larger than the stored energy in the battery.

Therefore, the ICE cycles ON and OFF to maintain a high SOC for the first part of the

drive cycle. When the remaining predicted energy requirement is smaller than the energy

stored in the battery, the ICE stays off to allow full discharge. In this example, the actual

energy used on Day 12 is less than the predicted energy and therefore the SOC is at 30%

at the end of the day.

Day 12: Usage and prediction graph Usage and prediction graph 2.5

Moving average for last Predicted energy for eleven days (kWh) Day12 (kWh) 1.5

Day 1 11

0.5 Energy usage in kWh kWh in usage Energy Energy inusage kWhr (x)

0 Deviation from Actual Energy mean (kWh) -0.5 1 Days Last 11 days

Figure 5.17: Eleven days energy usage pattern and predicted energy for Day 12

SOC vs time: Day:12 100

(%) SOC 60

(%) charge ofState 40

30 0 200 400 600 800 1000 1200 1400 Time Figure 5.18: State of charge profile on Day 12

Day 13:

The energy predicted for Day 13 is the average for the last eleven days since the energy used in previous four days varies more than Ə∆ $'4 and Ə∆( ) . The actual energy used on Day 13 is however greater than the predicted energy and hence, the SOC cycles at the lower end of SOC band (Figure 5.20). Usage and prediction graph 2.5

1.5 Moving average for last Predicted energy for eleven days (kWh) Day13 (kWh)

1 DAY 0.5 12

Energy usage in kWhr (x) kWhr in usage Energy 0

Energy usage in kWh kWh in usage Energy Deviation from -0.5 mean (kWh) Actual Energy -1 1 Last 11Days days Figure 5.19: Eleven days energy usage pattern and predicted energy for Day 13

SOC vs time: Day:13 100

50 SOC (%) SOC

(%) charge ofState 30

10 0 100 200 300 400 500 600 700 800 TimeTime (seconds) Figure 5.20: State of charge profile on Day 13 94

Day 14:

The energy usage in previous four days is significantly different. Therefore, the average for the last eleven days is predicted for Day 14 (Figure 5.21). On Day 14, the actual energy used by the vehicle is higher than the predicted energy. The SOC cycles at the lower end after reaching the minimum SOC set point (Figure 5.22).

Usage and prediction graph 3 Predicted energy for Day14 (kWh) 2.5

1.5 Moving average for last eleven days (kWh) DAY 1 13 DAY 0.5 12

Energy usage in kWhr (x) kWhr in usage Energy kWh in usage Energy 0

-0.5 Deviation from mean (kWh) Actual Energy -1 1 LastDays 11 days Figure 5.21: Eleven days energy usage pattern and predicted energy for Day 14

SOC vs time: Day:14 100

50 SOC (%) SOC

40 State of charge (%) (%) charge ofState

10 0 100 200 300 400 500 600 700 800 Time Figure 5.22: State of charge profile on Day 14 95

Day 15:

The energy usages in the previous four days are not within ∆( ) for all four days.

Therefore, the average energy of previous eleven days is the predicted energy for Day 15

(Figure 5.23). On Day 15, the actual energy used by the vehicle is higher than the predicted energy. The SOC cycles at the lower band after reaching the lower set point

(Figure 5.24).

Usage and prediction graph 3 Predicted energy for 2.5 Day15 (kWh)

1.5 Moving average for last DAY DAY eleven days (kWh) 1 13 14 DAY 0.5 12

Energy usage in kWhr (x) kWhr in usage Energy Energy usage in kWh kWh in usage Energy 0

-0.5 Deviation from mean (kWh) Actual Energy -1 1 LastDays 11 days Figure 5.23: Eleven days energy usage pattern and predicted energy for Day 15

SOC vs time: Day:15 100

50 SOC (%) SOC

State of charge (%) (%) charge ofState 30

10 0 100 200 300 400 500 600 700 800 Time Figure 5.24: State of charge profile on Day 15 96

Day 16:

The energy usage in at least one of the last four days is outside the ∆( ) band; the average energy of eleven days (stored) is the predicted energy for Day 16 (Figure 5.25).

On Day 15, the actual energy used by the vehicle is higher than the predicted energy. The

SOC cycles at the lower band after reaching the lower set point (Figure. 5.26).

Usage and prediction graph 3 Predicted energy for Day16 (kWh) 2.5

1.5 Moving average for last DAY DAY DAY eleven days (kWh) 1 13 14 15 DAY 0.5 12 Energy usage in kWh kWh in usage Energy

(x) kWhr in usage Energy 0

Deviation from -0.5 mean (kWh) Actual Energy -1 1 Last 11Days days Figure 5.25: Eleven days energy usage pattern and predicted energy for Day 16 SOC vs time: Day:16 100

90 80

50 . (%) SOC 40

30 State of charge (%) (%) charge ofState

10 0 100 200 300 400 500 600 700 800 Time

Figure 5.26: State of charge profile on Day 16

Day 17:

The predicted energy is the average of the last four days on Day 17, since the new daily drive cycle (HWFET with only ±5% variation) has been used for the previous four consecutive days (Figure 5.27). Figure 5.28 shows that the algorithm adapts to the new drive cycle with the SOC reaching 20% at the end of the day.

Usage and prediction graph 3 Moving average for last Predicted energy for eleven days (kWh) Day17 (kWh) 2.5

1.5 DAY DAY DAY DAY

1 13 14 15 16 DAY 0.5 12 Energy usage in kWhr (x) kWhr in usage Energy

kWh in usage Energy 0

-0.5 Deviation from mean (kWh) Actual Energy -1 1 Days Last 11 days Figure 5.27: Eleven days energy usage pattern and predicted energy for Day 17

SOC vs time: Day:17 100

90 80

60 SOC (%) SOC 50

40 State of charge (%) charge State of

20 0 100 200 300 400 500 600 700 800 Time Figure 5.28: State of charge profile on Day 17 98

Day 18:

The daily drive cycle was changed from HWFET to UDDS cycle for Day 18 to further test the adaptability of the algorithm. The algorithm, based on the history, assumes the drive cycle to be the same as in the previous day and therefore predicts the average energy used in the previous four days. The energy predicted is higher than the actual energy used on Day 18 and the battery is only discharged to 80% SOC (Figure 5.30).

Usage and prediction graph 3 Moving average for last Predicted energy for Day18 (kWh) 2.5 eleven days (kWh)

2 DAY DAY DAY DAY DAY 1.5 13 14 15 16 17 1 DAY

0.5 12

(x) kWhr in usage Energy

kWh in usage Energy 0

-0.5 Deviation from Actual Energy mean (kWh) -1 1 LastDays 11 days

Figure 5.29: Eleven days energy usage pattern and predicted energy for Day 18

SOC vs time: Day:18 100

60 SOC (%) SOC 50

40 State of charge (%) (%) charge ofState

20 0 200 400 600 800 1000 1200 1400 Time Figure 5.30: State of charge profile on Day 18 99

The driver uses the new daily drive cycle for the next four days (Days 19, 20, 21, 22).

The algorithm adapts to the new daily drive cycle on Day 22 (Figure 5.32). The state of charge profile on Day 22 is shown in Figure 5.31. The predicted energy is almost equal to the energy used and the battery is discharged to 20% at the end of the day.

Usage and prediction graph 3 Moving average for last eleven days (kWh) Predicted energy for Day22 (kWh) 2.5

DAY DAY DAY DAY DAY 1.5 13 14 15 16 17 DAY 1 DAY DAY DAY DAY

kWh in usage Energy 12 18 19 20 21

Energy in usage kWhr (x) 0.5

0 Deviation from mean (kWh) Actual Energy -0.5 1 ElevenLast days 11 of days stored data

Figure 5.31: Eleven days energy usage pattern and predicted energy for Day 22

100

SOC vs time: Day:16 100

SOC (%) SOC 50

40 (%) charge ofState 30

20 0 200 400 600 800 1000 1200 1400 Time Figure 5.32 State of charge profile on Day 22

Figure 5.33 shows the actual energy and the predicted energy for each day. Figure 5.33 also shows the adaptation of ̿+- (predicted energy) following the daily drive cycle pattern according to the moving average algorithm of Figure 5.3.

Actual vs predicted Actual Predicted 3

2.5 2

1.5

Energy (kWh) Energy 0.5

0 12 13 14 15 16 17 18 19 20 21 22 Day

Figure 5.33: Predicted energy vs. Actual energy used

101

5.3 CONCLUSION

The energy usage adaptation strategy uses the energy predicted based on the data collected over the previous eleven days and adjusts the battery charge/discharge pattern.

The algorithm adapts to the driver patterns to bring down the battery SOC as close as possible to the minimum allowed SOC (SOC min ) at the end of the day. It will thus be possible to charge the battery from the grid electricity as much as possible. The incentive to make greater use of grid energy comes from the fact that the cost per mile of operation using grid energy is less than that of using fossil fuel energy [20]. The algorithm also allows only one deep discharge cycle of the battery each day, which helps increase the life of the battery.

Two types of prediction algorithm for predicting the energy usage based on previous energy usage data have been presented. The moving average based algorithm is found to be suitable when the vehicle is gradually adapting to a new drive cycle. The exception algorithm is preferred if the vehicle generally follows a standard pattern of daily drive cycles and occasionally switches to a different daily drive cycle for a day or two. This may be the case, for example, if the driver uses the vehicle to commute to his workplace for five days following almost the same drive cycle everyday and then switches to a different daily drive cycle on weekends. In practice, the selection of the particular algorithm could be set in the controller either by the user or in the factory.

The prediction algorithm is followed by an energy management algorithm that adjusts the SOC limits of the vehicle controller. The two algorithms together constitute the energy usage adaptation strategy. The adaptation strategy is expected to result in cost and component benefits for plug-in hybrid vehicles. The next chapter presents simulation

102 results to analyze the benefits of using the adaptation algorithm in the series and the series-parallel plug-in hybrid vehicle from both the consumer and ecological points of view.

103

CHAPTER VI

ANALYSIS OF ENERGY ADAPTATION

The plug-in hybrid vehicle can be advantageous both from an economical as well as from an ecological perspective. The economical benefits can be evaluated through fuel economy and cost per mile calculations, while well-to-wheel energy usage and efficiency are measures for the ecological benefits.

6.1 FUEL ECONOMY AND COST PER MILE

Fuel economy of a plug-in hybrid vehicle is measured according to the SAE standard

SAEJ1711 [21] which specifies the SAE recommended practice for measuring emissions and fuel economy in hybrid vehicles. SAEJ1711 states that the total fuel consumed in gallons (US) during the drive cycle for a plug-in hybrid vehicle is the sum total of the onboard fuel consumed and the fuel equivalent value of the supplied off-board energy.

The off-board energy supplied (from the wall) is the amount required at the end of the drive cycle to restore the energy storage system to its initial SOC. The total off-board supplied energy (kWh) is divided by the gasoline equivalency factor to compute the equivalent amount of fuel consumed. The fuel economy FE is given by

FE (mpg) = Distance travelled (miles) / TF (gallons)

104 where TF = Total fuel consumed = G + Gequiv (6.1)

Here Gequiv = E ob / v e ; (6.2)

G is the onboard fuel consumed in gallons (US);

Gequiv is the gasoline equivalent of off-board energy in gallons (US);

Eob is the off-board energy in kWh used to recharge the energy storage system;

ve is the energy (kWh) per gallon of gasoline equivalent and is equal to 38.322

kWh/gallon;

The cost per mile (CPM) during the drive cycle can be obtained from the total cost of driving the vehicle for a certain number of miles. The total cost is given by

Total cost = G × Cost/gallon + E ob × Cost/kWh (6.3)

Assuming a cost of $3 per gallon for gasoline and $0.0959 per kWh of household

electricity (based on Energy Information Administration, data for Ohio residential cost

per kWh in 2007), the total cost is given by

Total cost = G × 3 + E ob × 0.0959 (6.4)

The cost per mile ($/mile) (CPM) is given by

CPM ($/mile) = Total cost / Distance travelled in miles (6.5)

Several simulations have been done to study the benefits of the series and series- parallel configurations with the energy usage adaptation technique. The benefits of the adaptation strategy over the no-adaptation strategy on the basis of fuel economy and cost

105 per mile have also been evaluated. Since the simulation with a 52-Ah battery requires extremely long simulation time and several multiple drive cycles, a 10-Ah and a 20-Ah battery has been used for simulation. The size of the battery has no other implication except that the ZEV range is reduced. The mass of the vehicle has also been adjusted accordingly for simulation.

The well-to-wheel efficiency is calculated for both the series and the series-parallel plug-in hybrids. The tank-to-wheel efficiency is calculated from the simulations and then multiplied with the well-to-tank efficiency obtained from the GREET model to get the overall well-to-wheel efficiency.

For fuel economy and cost per mile calculations, the standard SAE drive cycles,

UDDS and HWFET will be used. The UDDS and HWFET cycles run distances of 7.5 and 10.26 miles, respectively. For longer tests, the vehicle is simulated over four consecutive HWFET cycles, which cover a distance of about 41 miles and is called

“HWFET_4cycles.” A combination of two HWFET and two UDDS cycles, giving a total distance of 32 miles, has been used to simulate a long drive cycle and is termed as

“UDDS_2HWFET_UDDS” cycle. The moving average based prediction algorithm is used for simulation.

6.2 RESULTS: CUSTOMER PERSPECTIVE

The simulations presented in this section focuses on the comparison between the adaptation and no-adaptation rule for the series and series-parallel configurations from

106 the economical aspects. The highway driving schedule (HWFET) is used for simulation.

The vehicle is simulated for a five day period and it is assumed that the drive cycle remains the same for all five days. A 10-Ah battery is used for the simulation. The initial data for the adaptation rule is obtained from different drive cycles with varying energy usage. The daily energy usage in the stored initial data is varied each day to account for different energy usages of the daily drive cycles. The moving average based prediction algorithm is used for simulation.

The engine, motor and generator output torques and the consequent battery SOC for the series plug-in hybrid vehicle with adaptation is shown in Figure 6.1 for a HWFET cycle. The SOC is maintained within 100%-90% band initially by turning the engine ON and OFF. The lower limit is set at 90% until the available energy from the battery and the required energy calculated from the algorithm are equal. The plot shows a single deep discharge cycle in battery only mode towards the end of the drive cycle which is the characteristic feature of the developed algorithm. The primary objective is to bring the end of day SOC as low as possible through adaptation to the daily driving patterns. The negative torque commands for the generator imply that it is used for starting the engine.

The engine, motor and generator output torques and the battery SOC for a series- parallel plug-in hybrid vehicle with adaptation is shown in Figure 6.2. Similar to the series vehicle, the battery undergoes one deep discharge period towards the end of the cycle. The SOC is maintained within 100%-90% initially by turning the engine ON and

OFF.

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Speed (mph) 0

-20

100

60 SOC SOC (%)

250

200

150

100

Engine Torque (Nm) 50

-20 Generator Torque (Nm) -40

1000 800 600

400

200 0 Motor Torque(Nm) -200 0 100 200 300 400 500 600 700 800 Time (seconds) Figure 6.1: Simulation result of the series plug-in hybrid with adaptation strategy in the HWFET cycle 108

Speed (mph) 0

-20

100

60 SOC SOC (%) 40

150

100

50 Engine Torque (Nm) 0

-10

-20 Generator Torque (Nm) -40 1000 800

600

400

200

Motor Torque(Nm) 0 -200 0 100 200 300 400 500 600 700 800 Time Figure 6.2: Simulation result of the series-parallel plug-in hybrid with adaptation strategy in the HWFET cycle

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In the following sets of simulation results, the focus is on the performance of the

EUA. Hence, the most important result from the vehicle simulation model is the SOC information. The SOC profiles from different simulations will be analyzed with the EUA strategy results. Table 6.1 gives the summary of the simulation results for the series plug- in hybrid whereas Table 6.2 gives the summary results for the series-parallel plug-in hybrid vehicle. Figures 6.3 through 6.6 gives the graphical representation of the data provided in Table 6.1 and Table 6.2. Figure 6.3 and Figure 6.5 show the actual and the predicted energy for the series and the series-parallel plug-in hybrid vehicle for the five days. Figure 6.4 and Figure 6.6 show that the fuel economy is fairly constant for the no- adaptation rule for the series and the series-parallel plug-in hybrid vehicle for the five days. This is because the end of day state of charge does not vary significantly for all these days. In other words the SOC profile remains almost same irrespective of the variation in speed or length of the drive cycle. In case of the adaptation strategy, the vehicle adapts to the new daily drive cycle after using it for four consecutive days. The fuel economy is observed to improve with adaptation on the fifth day when the algorithm adapts to the new drive cycle (HWFET).

For the series plug-in hybrid vehicle the fuel economy during the first four days is almost the same with adaptation and no-adaptation, as seen in Figure 6.4. The adaptation algorithm predicted the energy usage correctly on the fifth day (Figure 6.3).

Consequently, the fuel economy is improved by 30%.

For the series-parallel plug-in hybrid simulation shown in Figure 6.6, the fuel economy for the first four days is higher without adaptation than with adaptation. This is because the end of day SOC was already quite low without adaptation. The engine 110

operates for a longer period of time during these days because the large difference in the

predicted and the actual energy usage. The fuel economy, however improves on the fifth

day. The adaptation algorithm helped bring the e nd of day SOC to a lower value on the

fifth day resulting in improved fuel economy. The improvement is intimately tied to the

battery SOC at the end of the daily cycle. The lower the end of daily cycle SOC, the

better will be the fuel economy. The adaptati on algorithm ensures that the end of daily

cycle SOC will be near its minimum value after it identifies the daily drive cycle. Table 6.1: Simulation results for series plug -in HEV with adaptation and no -adaptation strategy

SOC SOC Fuel Fuel economy HWFET Distance Predicted Actual kWh (end of (end of cycle) Economy (No Day Scaling travelled energy consumed cycle) (No (mpg, with adaptation) factor (miles) (kWh) (kWh) (Adaptation) Adaptation) adaptation) (mpg)

1 1 10.2363 67.31 67.09 3.3852 2.5284 36.48 36.61 2 1.01 10.3386 61.38 67.76 3.3169 2.5818 37.61 36.60 3 1.02 10.4409 55.55 68.44 3.2447 2.6359 38.75 35.60 4 1.01 10.3386 54.83 67.76 3.158 2.5818 39.44 36.60 5 0.998 10.2158 31.6 67.08 2.5919 2.5718 48.17 36.80

Predicted

Actual Energy (kWh) Energy

Day Figure 6.3: Predicted and actual energy consumption during adaptation

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A: End of cycle SOC with adaptation B: End of cycle SOC with out adaptation

A B B B B B MPG (Adaptation) A

A MPG (No Adaptation) A

Day Figure 6.4: Fuel economy and end of cycle state of charge for adaptation and non adaptation rule.

Table 6.2: Simulation results for series -parallel plug-in HEV with adaptation and no -adaptation strategy

SOC Fuel SOC Actual Fuel HWFET Distance (end of Predicted economy (end of kWh Economy Day Scaling travelled cycle) energy (No cycle) consumed (mpg, with factor (miles) (No (kWh) adaptation) (Adaptation) (kWh) adaptation) Adaptation) (mpg)

1 1 10.2363 97.064 51.60 4.6273 2.9891 31.18 36.63 2 1.01 10.3386 93.042 52.12 4.5271 2.9486 32.16 36.50 3 1.02 10.4409 90.55 52.64 4.3861 3.0598 32.56 35.90 4 1.01 10.3386 88.5 52.12 4.2279 2.9486 33.27 36.50 5 0.998 10.2158 33.5 51.59 2.9738 2.9071 46.31 36.85

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Predicted

Actual Energy (kWh) Energy

Day

Figure 6.5: Predicted and actual energy consumption during adaptation

A: End of cycle SOC with adaptation B: End of cycle SOC with out adaptation

A MPG (Adaptation) A

A B B B B B

A MPG (Non Adaptation)

Day Figure 6.6 : Fuel economy and end of cycle state of charge for adaptation and no -adaptation algorithms.

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The next set of simulations studies the state of charge profile, fuel economy and cost per mile for all of the daily drive cycles with a 10-Ah battery and a 20-Ah battery.

The results assume that the predicted value of the energy required is accurate. The drive cycle scaling factor for all the simulations is set to 1. The simulation results with the series configuration are given in Table 6.3.

Figures 6.7 through 6.14 show the SOC profiles for the series plug-in hybrid vehicle for the different drive cycles. In case of the no-adaptation algorithm, SOC depletes to the lower limit and the engine is turned ON to charge the battery. In case of the adaptation algorithm, the state of charge is maintained within a narrow band until the predicted energy is equal to the energy available from the battery. The engine is turned

OFF and the battery supplies the required power at the wheels. The HWFET cycle consumes more energy compared to the UDDS cycle and so the battery discharges faster in the HWFET cycle.

The fuel economy and cost per mile operation are found to improve with the adaptation technique although to a varying degree depending on the daily drive cycle. In case of the UDDS, HWFET and UDDS_2HWFET_UDDS cycles, the battery is efficiently utilized by the adaptation algorithm. The engine operation is optimized by the proper prediction of the energy usage. Hence, the fuel economy is high compared to the no-adaptation technique.

In the case of HWFET_4cycles drive cycle (Figure 6.11 and Figure 6.12), the

battery utilization is the same for both the algorithms. The fuel economy and cost per

mile are, therefore, almost equal for both algorithms. The only difference is the number

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of deep discharge cycles sustained by the battery during the drive cycle. The adaptation

algorithm allows only one deep discharge during the drive cycle which extends the life

of the battery while the no-adaptation algorithm causes the battery to undergo two deep

discharge cycles.

Table 6.3 : Fuel economy and Cost per mile for series plug -in HEV

Series Configuration Battery No adaptation Adaptation Distance Cost Cost Size Cycle Fuel Deep Fuel Deep (miles) per per (Ah) Economy discharge Economy Discharge mile mile (mpg) cycles (mpg) cycles ($/mile) ($/mile) 10 UDDS 7.45 27.53 0.11 1 35.91 0.1 1 10 HWFET 10.28 37.87 0.08 1 45.24 0.07 1 20 HWFET_4cycles 40.94 37.02 0.08 2 37.46 0.08 1 20 UDDS_2HWFET_UDDS 32.06 31.36 0.1 1.5 33.92 0.09 1

SOC vs time: Day:13 100 100

90 90

80 80 70 70 60 60 50 SOC (%) SOC (%) 50 40

30 40

20 30 10 0 200 400 600 800 1000 1200 1400 20 Time 0 200 400 600 800 1000 1200 1400 Time(seconds) Figure 6.7: State of charge profile for the Figure 6.8: State of charge profile for the series plug-in HEV without adaptation series plug-in HEV with adaptation during the UDDS cycle during the UDDS cycle

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SOC vs time: Day:12 SOC vs time: Day:12 100 100

90 90

80 80 70 70 60 60 50 SOC (%) SOC (% ) 50 40

30 40

20 30

10 0 100 200 300 400 500 600 700 800 20 0 100 200 300 400 500 600 700 800 Time Time Figure 6.9: State of charge profile for the Figure 6.10: State of charge profile for series plug-in HEV without adaptation the series plug-in HEV with adaptation during the HWFET cycle during the HWFET cycle

SOC vs time: Day:12 SOC vs time: Day:12 100 100

90 90

80 80

70 70 60 60 50 SOC (%) SOC (%) 50 40

30 40

20 30

10 20 0 500 1000 1500 2000 2500 3000 3500 0 500 1000 1500 2000 2500 3000 3500 Time Time

Figure 6.11: State of charge profile for the series Figure 6.12: State of charge profile for the plug-in HEV without adaptation for four series plug-in HEV with adaptation for four consecutive HWFET cycles consecutive HWFET cycles

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SOC vs time: Day:13 SOC vs time: Day:13 100 100

90 90

80 80

70 70 60 60 50 SOC (% ) SOC (% ) 50 40

40 30

20 30

10 20 0 500 1000 1500 2000 2500 3000 3500 4000 0 500 1000 1500 2000 2500 3000 3500 4000 Time Time Figure 6.13: State of charge profile for the series Figure 6.14: State of charge profile for plug-in HEV without adaptation for a the series plug-in HEV with adaptation combination of HWFET and UDDS cycle for a combination of HWFET and UDDS cycle

The simulation results with the series-parallel configuration are given in Table

6.4. Figures 6.15 through 6.22 show the SOC profile for the series-parallel plug-in hybrid

vehicle. The 42-kW engine supplies power to the wheels during high acceleration

demand and also to charge the batteries through the 27-kW generator. The adaptation

algorithm gives higher fuel economy and lower cost per mile than the no–adaptation

algorithm. The fuel economy is found to be almost the same for

UDDS_2HWFET_UDDS cycle. This is because the end of daily cycle SOC for both the

adaptation and no-adaptation algorithms reaches the minimum SOC limit.

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Table 6. 4: Fuel economy and Cost per mile for series -parallel plug -in HEV

Series-Parallel Configuration

No adaptation Adaptation Battery Distance Size Cycle (miles) (Ah) Fuel Deep Fuel Deep Cost/mile Cost/mile Economy discharge Economy Discharge ($/mile) ($/mile) (mpg) cycles (mpg) cycles

10 UDDS 7.45 30.04 0.1 2 32.91 0.9 1

10 HWFET 10.28 36.63 0.08 2 45.38 0.07 1

20 HWFET_4cycles 40.94 36.92 0.08 2 36.46 0.08 1

20 UDDS_HWFET_UDDS 32.06 29.66 0.1 1.5 33.27 0.09 1

SOC vs time: Day:13 100 100

90 90

80 80 70

70 60

50 60 SOC (% )

40 50 30 40 20 10 30 0 200 400 600 800 1000 1200 1400 0 200 400 600 800 1000 1200 1400 Time

Figure 6.15: State of charge profile for the Figure 6.16: State of charge profile for the series-parallel plug-in HEV without series-parallel plug-in HEV with adaptation during UDDS cycle adaptation during UDDS cycle

118

SOC vs time: Day:12 SOC vs time: Series-parallel with 10Ah 100 100

90 90

80 80

70 70 60 60

50 SOC (% ) SOC (% ) 50 40 40 30

30 20

10 20 0 100 200 300 400 500 600 700 800 0 100 200 300 400 500 600 700 800 Time Time (seconds) Figure 6.17: State of charge profile for Figure 6.18: State of charge profile for the the series-parallel plug-in HEV without series-parallel plug-in HEV with adaptation during HWFET cycle adaptation during HWFET cycle

SOC vs time: Day:12 SOC vs time: Day:12 100 100

90 90

80 80

70 70 60 60 50 SOC (% ) SOC (% ) 50 40

40 30

20 30

10 20 0 500 1000 1500 2000 2500 3000 3500 0 500 1000 1500 2000 2500 3000 3500 Time Time

Figure 6.19: State of charge profile for Figure 6.20: State of charge profile the series-parallel plug-in HEV for the series-parallel plug-in HEV without adaptation for four with adaptation for four consecutive consecutive HWFET cycles HWFET cycles

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Series-Paraallel no adaptation 100 100

90 90

80 80

70 70 60 60

SOC(% ) 50 SOC(% ) 50 40

30 40

20 30

10 0 500 1000 1500 2000 2500 3000 3500 4000 20 0 500 1000 1500 2000 2500 3000 3500 4000 Time (seconds) Time(seconds) Figure 6.21: State of charge profile for Figure 6.22: State of charge profile for the series-parallel plug-in HEV without the series-parallel plug-in HEV with adaptation for four consecutive adaptation for four consecutive HWFET cycles . HWFET cycles.

6.3 RESULTS: ECOLOGICAL PERSPECTIVE

The well-to-wheel efficiency is the product of the well-to-tank-efficiency and the tank-to-

wheel efficiency. The well-to-tank efficiency is calculated from the GREET model and

the tank-to-wheel efficiency is calculated from the vehicle simulation model developed in

MATLAB/Simulink.

The procedure for calculating the well-to-tank efficiency for a plug-in hybrid

vehicle has been presented in Chapter 2. In this chapter, the well-to-tank efficiency is

calculated for a series plug-in HEV operating in different drive cycles.

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The well-to-tank efficiency will be different for various drive cycles due to the difference in the ratios of grid energy and gasoline energy usage. The grid energy and gasoline energy usage ratio is related to the charge depleting (CD) and the charge sustaining (CS) modes of the vehicle. The CD and CS mode usage is calculated for each drive cycle and the data is used in the GREET model to calculate the well-to-tank efficiency. Table 6.5 shows the well-to-tank efficiency calculation of the series plug-in

HEV for different drive cycles.

Table 6.5: Well-to-tank efficiency calculations for the series plug-in HEV

Well-to- Battery Total CD Grid Gasoline CS mode tank size Cycle Distance time Mode Usage Usage (seconds) efficiency (Ah) (seconds) (seconds) (%) (%) (η2)

20 HWFET_4cycles 40.94 3000.00 2200.00 800.00 27 73 0.68

20 UDDS_HWFET_UDDS 32.06 3520.00 2250.00 1270.00 36 64 0.66

10 HWFET 10.24 764.00 380.00 384.00 50 50 0.58

10 UDDS 7.39 1372.00 250.00 1122.00 82 18 0.46

The tank-to-wheel efficiency is the ratio of the energy used at the wheels to the total

energy supplied. The total energy supplied is the sum total of the energy from the on-

board fuel consumed and the off-board electrical energy. The tank-to-wheel

efficiency calculated from the vehicle simulation is shown in Table 6.6.

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Table 6.6: Tank-to-wheel efficiency calculations for the series plug-in HEV

Series Configuration with adaptation

kWh: Off- Total Actual kWh: Tank-to- Battery Distance Fuel board energy Energy Cycle engine wheel size (Ah) (miles) Consumed electricity (kWh) consumed (G) efficiency (gallons) (Eob) (G + : wheels (kWh) (η ) (kWh) Eob) (kWh) 1

20 HWFET_4 cycles 40.94 1.01 36.70 3.17 39.87 10.11 0.25 UDDS_HWFET_ 20 32.06 0.86 31.07 3.45 34.52 7.78 0.23 UDDS 10 HWFET 10.24 0.21 7.63 1.58 9.21 2.53 0.27

10 UDDS 7.39 0.22 7.99 1.64 9.64 2.29 0.24

In the HWFET cycle, the grid and gasoline energy is shared equally (50%). The well-

to-tank efficiency calculated from the GREET model is then 58%. The energy

equivalent from on-board fuel supplied during the drive cycle is 7.63 kWh and the

off-board energy used to recharge the battery to 100% state of charge is 1.58 kWh.

The actual energy used to drive the vehicle is 2.53 kWh which gives a tank-to-wheel

efficiency of 27%. Hence, the overall well-to-wheel efficiency in the HWFET cycle

for the series plug-in HEV is 16% (well-to-tank efficiency (58%) x tank-to-wheel

efficiency of (27%)). The well-to-wheel efficiency of different drive cycles for the

series plug-in HEV are shown in Figure 6.23.

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0.68 0.70 0.66 Well-to-tank efficiency 0.58 Tank-to-wheel efficiency 0.60 Well-to-wheel efficiency 0.50 0.46

0.40

0.25 0.27 0.30 0.23 0.24 0.17 0.20 0.15 0.16 0.11 0.10

0.00 HWFET_4cycles UDDS_2HWFET_ HWFET cycle UDDS cycle UDDS cycles

Figure 6.23: Well -to-wheel efficiency of the series plug-in HEV

The maximum overall well -to-wheel efficiency for the series plug -in HEV is 17%.

This efficiency is lower than the well-to-wheel efficiency of conventional ICE vehicles (19%) and electric vehicles (20%) [2]. The ICE vehicles have higher well -to- tank efficiency compared to plug -in hybrid vehicles while the electric vehicles have higher tank-to-wheel efficiency compa red to plug-in hybrid vehicles. The lower well- to-wheel efficiency can be attributed to the poor well-to-tank efficiency of the plug -in hybrid vehicles. The well -to-tank efficiency is found to decrease with the increase d share of grid energy in the total e nergy usage of the vehicle. This is because the electricity generation in US is based on fossil fuels which have a very low efficiency transportation pathway and high GHG emissions. This can be improved by using nuclear fuel or other renewable energy sourc es.

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6.4 SUMMARY OF RESULTS

Table 6.7: Fuel economy and cost per mile for series and series-parallel PHEV

Series-Parallel Series configuration configuration with with adaptation adaptation Battery Distance Size (Ah) Cycle (miles) Fuel Fuel Cost/mile Cost/mile Economy Economy ($/mile) ($/mile) (mpg) (mpg)

10 UDDS 7.45 35.91 0.1 32.91 0.9 10 HWFET 10.28 45.24 0.07 45.38 0.07 20 HWFET_4cycles 40.94 37.46 0.08 36.46 0.08 20 UDDS_HWFET_UDDS 32.06 33.92 0.09 33.27 0.09

The simulation results show that the series plug-in hybrid vehicle gives higher fuel economy and better cost per mile than the series-parallel configuration for all the drive cycles (Table 6.7). The highway driving cycles, HWFET and HWFET_4cycles, gives higher fuel economy than the city driving cycles like UDDS. It was observed that the components selected for both configurations are able to sustain the power demand of the drive cycles.

The adaptation algorithm gives better fuel economy and cost per mile compared to the no-adaptation algorithm. It was also observed the fuel economy during the adaptation may be lower than the no-adaptation algorithm but improves as the driver continues to use the same the drive cycle. The adaptation algorithm also ensures only one deep-discharge cycle of the battery per day. The series plug-in HEV is found to have a well-to-wheel efficiency between 11%-17% which is comparatively lower than the conventional ICE and electric vehicles.

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CHAPTER VII

CONCLUSION AND FUTURE WORK

7.1 SUMMARY AND CONCLUSIONS

In recent years problems like global warming, high fuel prices and depletion of fossil fuel based resources have triggered immense interests in hybrid and zero-emission vehicles.

Stringent emission standards have forced many automakers to increase their efforts on research and development of hybrid electric vehicles especially grid-connected vehicles like the plug-in hybrids. The motivations for the development of hybrids are to reduce emission of greenhouse gasses, reduce our dependency on fossil fuels and achieve higher fuel economy.

Two types of plug-in hybrid vehicle configurations have been studied in this thesis: series and series-parallel configurations. The sizings of the components of the series and series-parallel plug-in hybrid vehicles have been addressed. Next, an energy usage adaptation strategy (EUA strategy) has been developed for the plug-in hybrids which attempts to predict the daily energy usage based on the driving pattern history. The predicted energy is used to maximize the grid energy usage of the vehicle. The series and the series-parallel plug-in hybrid vehicle model and the EUA strategy has been designed and implemented in the MATLAB/Simulink ® platform.

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7.1.1 PLUG-IN HEV DESIGNS

The series and the series-parallel drivetrains have been designed based on two constraints: the performance constraint that require 0-60 mph in less than 12 sec and the ecological constraint that requires a 40-mile zero-emission range. The traction motor in the series drivetrain is designed on the basis of the performance constraint. The engine and generator are rated to sustain a cruising speed of 70 mph at 1% gradient. Theoretical calculations and simulations show that a 100-kW (peak power) motor along with a 42- kW engine/generator system will be able to achieve the desired performance. A 52-Ah battery with a peak power of 100 kW is required to achieve the desired zero-emission range.

The series-parallel drivetrain configuration uses the engine to supply power in parallel with the traction motor during peak power demand. This reduces the size of the electric machines, traction motor and generator, but at the same time adds one more component to the vehicle – the transmission. The increased vehicle mass due to the additional component is offset by the reduction in sizes of the traction motor and the generator. The engine size can be kept the same as the series plug-in hybrid. The generator is designed to sustain a cruising speed of 60 mph at 1% gradient. Theoretical calculations and simulations show that the traction motor and the generator can be downsized to 45 kW and 27 kW, respectively to achieve the desired performance. A 52-

Ah battery is required to achieve the 40-mile zero-emission range.

The control strategy developed for the series drive-train is simple and is suitable for driving in city and steady-speed driving cycles as long as the power requirement is less than 42 kW. The controller switches to series-mode operation once the state of

126 charge of the battery depletes to its lower limit. The control strategy for the series-parallel plug-in hybrid is more complex and offers three modes of operation – (i) parallel mode,

(ii) series mode and (iii) engine only mode-depending on the vehicle speed and the driver acceleration demand. The main objective of the design is to develop a tool to test the

EUA for a series and series-parallel plug-in hybrid vehicle.

7.1.2 ENERGY USAGE ADAPTATION

The primary motivation for plug-in hybrids is to substitute some of the onboard fossil fuel energy with off-board grid electrical energy. This thesis develops an energy usage adaptation (EUA) strategy to further the cause, and simultaneously, extend the life of the battery, improve fuel economy and reduce cost per mile of operation. The EUA strategy utilizes the total energy (wheels) usage data from the daily drive cycle to adjust the charge/discharge profile of the energy storage system. The strategy is implemented through a prediction and an energy management algorithm. The prediction algorithm works on a set of previously stored energy usage data of daily drive cycles. The prediction is based on a set of rules that allows the vehicle to adapt to a new driving route once that route has been repeated for four consecutive days. The predicted data is then used by the energy management algorithm to set the upper and lower state of charge limits of the battery. The energy management algorithm calculates the energy required for the day using the predicted energy and the actual energy used at the wheels.

Depending on this required energy and the available energy from the battery, the state of charge limits are set in the controller. The vehicle operates initially in the charge sustaining mode and then in the charge depleting mode once the required energy matches

127 the battery available energy. The energy management strategy attempts to have only one deep discharge cycle of the battery and keep the SOC at the minimum possible level at the end of the daily drive cycle. The battery is recharged at the end of the day using the low cost grid electricity to reduce the operational cost of the vehicle.

Simulation results with the EUA strategy for the series and the series-parallel plug-in hybrid vehicle shows that the vehicle successfully adapts to a new driving route at the end of four days. Four different daily driving cycles consisting of the UDDS and

HWFET cycles have been used to measure the fuel economy using the EUA strategy.

The simulation results with the 10-Ah and 20-Ah battery shows that the fuel economy improves when the EUA strategy adapts to the daily driving cycle. The EUA strategy also allows only one deep discharge cycle per day which reduces the stress on the battery.

The test results have shown a higher fuel economy in the highway driving cycles compared to the city driving cycles.

The series plug-in hybrid gives a slightly higher fuel economy than the series- parallel plug-in hybrid vehicle with the EUA strategy. The plug-in hybrid vehicle with the EUA strategy also gives low cost per mile of operation compared to no-adaptation strategy. The key to all the improvements is the end of cycle SOC. The lower the end of cycle SOC, the more will be the grid energy usage and more will be the fulfillment of the plug-in hybrid concept.

128

7.1.3 WELL-TO-WHEEL EFFICIENCY

A detailed analysis has been done for the series plug-in hybrid using the GREET model for a number of drive cycles. It has been found that the share of the grid and gasoline energy used during the drive cycle depends on the size of the battery and the control strategy. Results from the GREET model analysis show that the overall well-to-wheel efficiency of the plug-in hybrid vehicle is poor compared to conventional gasoline and electric vehicles. The overall well-to-wheel efficiency can be increased by improving the process of generation of electricity. Use of renewable energy sources like wind, solar or nuclear power can help eliminate the emissions and increase the efficiency.

7.2 CONTRIBUTIONS OF RESEARCH

The primary contributions of this thesis are:

Series and series-parallel plug-in HEV component sizing

Energy usage adaptation (EUA) strategy

Results and analysis of plug-in HEV with EUA.

7.3 FUTURE WORKS

The major contribution of this thesis is the novel strategy of energy usage adaptation based on the pattern of daily drive cycles of a plug-in hybrid vehicle. The strategy can be utilized to maximize the grid electricity usage which is the primary intent of using the plug-in hybrid. The strategy opens the door to the numerous possibilities that can be derived of this fundamental adaptation concept. Several adaptation strategies to predict the energy usage can be stored in the vehicle controller and either this could be factory

129 set or selected by a driver. For example, the driver may select a predefined drive cycle if he knows his route for the day. A plug-in hybrid buyer is likely to have an option of the battery size he or she will purchase, and the adaptation strategy could be set based on the

ZEV range of the vehicle. The prediction algorithm in the adaption strategy can also be modified to take care of the battery size.

A simplified model has been used to design the plug-in hybrid vehicles (series and

series-parallel). More components like the DC/DC converter for the accessories, plug-in

interface circuit and battery can be included in the vehicle model. Different fault modes

can also be added to improve the reliability of the model.

The well-to-wheel efficiency analysis using GREET model has been done for the

series plug-in hybrid. The well-to-tank efficiency analysis uses the fuel pathway for

conventional electricity generation using coal power plants. In the next decade, the focus

will shift towards more renewable energy sources for electricity generation. Hence, the

analysis needs to be extended to all other alternative fuel pathway options to project a

more reliable well-to-wheel efficiency. The analysis also needs to be extended to the

series-parallel plug-in hybrid vehicle for comparison with other drivetrain technologies.

One of the research areas that are gaining tremendous interests in recent years

regarding the plug-in hybrid vehicles is the vehicle-to-grid (V2G) concept. The plug-in

hybrid has the capability to store large amount of energy from the grid which can be

transferred back to the grid during power outages or grid power shortage. This may

reduce the operation cost and overall efficiency of the vehicle.

130

REFERENCES

[1] M. Ehsani, Y. Gao, S.E. Gay and A. Emadi, Modern Electric, Hybrid Electric, and Fuel Cell Vehicles: Fundamentals, Theory and Design, CRC, 2005.

[2] I. Husain, Electric and Hybrid Vehicles, Design Fundamentals, CRC, 2003.

[3] BP Statistical Review of World Energy, June 2007. Available: www.bp.com/statisticalreview

[4] T. Markel and A. Simpson “Plug-In Hybrid Electric Vehicle Energy Storage System Design,” National Renewable Energy Laboratory , NREL/CP-540-39614, May 2006.

[5] M.S. Duvall, “Battery Evaluation for Plug-In Hybrid Electric Vehicles,” IEEE Transactions on Vehicular Power and Propulsion , September 2005.

[6] A. Simpson, “Cost benefit analysis of a plug-in hybrid technology,” National Renewable Energy Laboratory , NREL/CP-540-40485, November 2006.

[7] F. Kreith, R.E. West and B.E. Isler, “Efficiency of Advanced Ground Transportation Technologies,” Journal of Energy Resources Technology , pp. 177, September 2002.

[8] J. Gonder and T. Markel, “Energy Management Strategies for Plug-In Hybrid Electric Vehicles,” National Renewable Energy Laboratory , NREL/CP-540-40970, April 2007.

[9] S. Drouilhet and B.L. Johnson, “A Battery Life Prediction Method for Hybrid Power Applications,” National Renewable Energy Laboratory , NREL/CP-440-21978, 1997.

[10] A. Pesaran, “Battery Choices for Different Plug-in HEV Configurations,” Plug-in HEV Forum and Technical Roundtable , National Renewable Energy Laboratory , NREL/PR-540-40378, July 2006.

[11] J.M. Miller, Propulsion Systems for Hybrid Vehicles, IEE, 2004.

131

[12] Green Car Congress, Plug-In Hybrid kits for Toyota and Ford Hybrids, Available: www.greencarcongress.com/2006/02/hymotion_unveil.html

[13] Chevy Volt Concept Car, Chevy Volt Specifications. Available: www.chevy-volt.net/chevrolet-volt-specs.htm

[14] JCN Newswire, “Japan Certifies Toyota Plug-in Hybrid for Public Road Tests,” July 2007. Available: www.japancorp.net/Article.Asp?Art_ID=14929

[15] R. Langari and J.S. Won, “Intelligent Energy Management Agent for a Parallel Hybrid Vehicle,” IEEE Transactions on Vehicular Technology , Vol. 54, May 2005, pp. 925-934.

[16] A. Rajagopalan and G. Washington, “Intelligent Control of Hybrid Electric Vehicles Using GPS Information,” Society of Automotive Engineers, 2002-01-1936, January 2002.

[17] M. Barth, G. Scora and T. Younglove, “Intelligent Off-board management of Vehicle Operating Parameters,” IEEE Transactions on Intelligent Transport Systems , Vol. 1, October 2003, pp. 352-357.

[18] Partnership of New Generation of Vehicles Organization, 1993. Available: www.pngv.org

[19] University of Akron Challenge X Team, “Technical Report #2: Vehicle Architecture Selection and Analysis,” 2004.

[20] EERE, US Department of Energy, “Alternative and Advance Vehicles”, Available: www.eere.energy.gov/afdc/vehicles/electric_what_is.html

[21] J. Gonder and A. Simpson, “Measuring and Reporting Fuel Economy of Plug-In Hybrid Electric Vehicles,” National Renewable Energy Laboratory , NREL/JA-540- 41341, 2007.

[22] C. Chan, “The state of the art of electric and hybrid vehicles,” in Proceedings of the IEEE , Vol. 90, No. 2, February 2002, pp. 247-275.

[23] S.S. Williams and A. Emadi, “Comparative Assessment of Hybrid Electric and Fuel Cell Vehicles Based on Comprehensive Well-to-Wheels Efficiency Analysis,” IEEE Transactions on Vehicular Technology , Vol. 54, No. 3, May 2005, pp. 856-862.

[24] K.L. Butler, M. Ehsani and P. Kamath, “A Matlab-Based Modeling and Simulation Package for Electric and Hybrid Electric Vehicle Design,” IEEE Transactions on Vehicular Technology , Vol. 48, No. 6, November 1999.

132

[25] N. Picot, “A Strategy to Blend Series and Parallel Modes of Operation in a Series- Parallel 2-2 Hybrid Diesel/Electric Vehicle,” MS Thesis, Electrical Engineering, University of Akron, December 2007.

[26] S. Golbuff, “Optimization of Plug-In Hybrid Electric Vehicle,” MS Thesis, Mechanical Engineering, Georgia Institute of Technology, August 2006.

[27] United States Environment Protection Agency, “Advanced Technology Vehicle Modeling in PERE,” Office of Transportation and Air Quality, March 2004.

[28] EPRI, “Plug-In Hybrid Technology Challenges,” September 2006. Available: www.arb.ca.gov/msprog/zevprog/symposium/presentations/duvall.pdf

133

APPENDICES

134

APPENDIX A

SERIES PHEV MODEL

LEVEL 1

135

136

LEVEL 2

[Shifter_pos] Shifter_pos P N R D [PNRD]

shif ter_pos Shif t_pos 1 [Shifter_pos] shif ter_pos motor_trq_cmd [motor_trq_cmd] shifter_pos [eng_on] 1 <> eng_on From7 v eh_spd 2 [veh_spd] v eh_spd [Ign_switch] Ign_switch [motor_trq_cmd] 2 veh_spd <> motor_trq_cmd From11

accel_pos <> gen_trq_cmd [gen_trq_cmd] 3 [accel_ped_pos] <> [brk_trq_cmd] 3 accel_ped_pos <> hydr_brk_trq_cmd From13 [accel_ped_pos] apedal [eng_trq_cmd] 4 brk_ped_pos <> 4 [brk_ped_pos] eng_trq_cmd brk_ped_pos From10 brk_ped_pos eng_trq_cmd [eng_trq_cmd] [gen_trq_cmd] 5 gen_trq_cmd [SOC_batt] SOC_batt From15 5 [Ign_switch] Ign_switch Ign_switch

6 [SOC_batt] eng_on [eng_on] SOC_batt [mot_brk_trq] motor_brk_trq

Propulsion

SOC_batt Out1 [mot_brk_trq]

[brk_ped_pos] Bpedal brake [brk_trq_cmd]

Brake

137

SCM BLOCK

LEVEL 3

PROPULSION BLOCK

138

LEVEL 4

Motor command

Eng_start_logic

Eng_gen_cmd 139

APPENDIX B

SERIES-PARALLEL PHEV MODEL

LEVEL 1

140

141

LEVEL 2

P N R D [Shifter_pos] Shif ter_pos neutral_select [neutral_select] Goto25

shif ter_pos 1 [Shifter_pos] shif ter_pos [eng_on] 1 <> shifter_pos eng_on Shif t_pos From7 motor_trq_cmd [motor_trq_cmd] [motor_trq_cmd] 2 v eh_spd <> 2 [veh_spd] motor_trq_cmd v eh_spd [Ign_switch] Ign_switch From11 veh_spd gen_trq_cmd [gen_trq_cmd] [brk_trq_cmd] 3 <> [accel_ped_pos] apedal hydr_brk_trq_cmd From13 eng_rpm 3 [eng_rpm] eng_rpm eng_rpm eng_trq_cmd [eng_trq_cmd] [eng_trq_cmd] 4 [veh_spd] v eh_spd <> eng_trq_cmd From10

accel_pos <> 4 [accel_ped_pos] eng_on [eng_on] <> [SOC_batt] SOC_batt [gen_trq_cmd] 5 <> accel_ped_pos gen_trq_cmd From15

[mot_brk_trq] motor_brk_trq shif ter_position [shifter_position] brk_ped_pos [shifter_position] 6 5 [brk_ped_pos] <> brk_ped_pos shifter_position brk_ped_pos From22 [Power] Total power shif t_dwn [shift_dwn] [shift_dwn] 7 <> shift_dwn From23 6 [Ign_switch] [eng_rpm] eng_rpm Ign_switch Ign_switch shif t_up [shift_up] [shift_up] 8 <> shift_up [neutral_select] neutral_select From24

[triptronic] 9 7 [SOC_batt] triptronic [triptronic] <> triptronic SOC_batt [Motorspd] Motorspd From25 Propulsion

8 [Power] Power

9 [Motorspd] Motorspd SOC_UC Motor_brk_trq [mot_brk_trq]

[brk_ped_pos] Bpedal brake [brk_trq_cmd]

Brake

SCM BLOCK

142

LEVEL 3

1 Shif ter eng_trq_cmd_powersplit [eng_trq_cmd_powersplit] Shift_pos

3 apedal apedal eng_trq_cmd_cruise [eng_trq_cmd_cruise]

6 motor_brk_trq [motor_trq_cmd] motor_brk_trq motor_trq_cmd 1 5 SOC_batt motor_trq_cmd SOC_batt

2 Ign_switch acceleration_mode [acceleration_mode] Ign_switch

[gear_actual] gear_actual Neutral [Neutral]

v eh_speed

eng_trq_cmd_city [eng_trq_cmd_city] Power

motor_through_road_parallel [motor_through_road_parallel] 10 Motorspd Motorspd Motor command

Ignition

[eng_trq_cmd_powersplit] eng_trq_cmd_powersplit

[eng_trq_cmd_cruise] eng_trq_cmd_cruise eng_start_cmd

[motor_trq_cmd] Motor_trq_cmd [motor_through_road_parallel] motor_through_road_parallel Eng_start_logic

eng_start_cmd eng_on 4 eng_trq_cmd_powersplit eng_on

[eng_trq_cmd_city] eng_trq_cmd_city gen_trq_cmd 2

eng_trq_cmd_cruise gen_trq_cmd eng_trq_cmd 3 eng_rpm eng_trq_cmd eng_gen_cmd [acceleration_mode]

4 v eh_spd shif ter_position 5 veh_spd shifter_position

parallel_power_req shif t_dwn 6 7 Total power shift_dwn

8 eng_rpm shif t_up 7 eng_rpm shift_up rev erse Tiptronic 8 triptronic

9 boolean neutral_select gear_act [gear_actual] neutral_select [Neutral] eng_of f gear_req Transmission Control

143

PROPULSION BLOCK

LEVEL 4

3 motor_brk_trq motor_brk_trq Motor_brk_trq_command Shif ter Motor_brk_trq_cmd

Motor brake torque Motor powersplit torque command 3 motor_trq_cmd

1 Shif ter Shifter Motor enable acceleration Motor accelaration enable 5 Ignition Ign_switch Total torque Motor powersplit torque

Accelerator pedal Acceleration mode 4 2 -K- Gear ratio Total Torque acceleration_mode apedal 9 Speed Engine parallel power torque commamnd Engine powersplit torque 1 6 Motorspd Engine parallel power torque commamnd eng_trq_cmd_parallel gear_actual SOC_batt

Mode 1:Parallel Mode

== 0

AND Cruise mode Engine cruise torque 6 Engine Cruise torque command 7 <= 60 eng_trq_cmd_series veh_speed Total torque Motor cruise torque Add1 Motor cruise torque command

4 SOC Bat Neutral 5 SOC_batt Neutral

Mode 2: Series Mode

2 eng_trq_cmd_mech Add == 0

AND Cruise mode

> 60 Engine Total torque

SOC Bat

Gear ratio Motor 8 Power Power Mode 3:Engine Only Mode

144

MODE SELECTION BLOCK

LEVEL 5

Mode1: Parallel Mode

Mode 2: Series Mode

Mode 3: Engine Only Mode

145

APPENDIX C

PREDICTION ALGORITHM CODE

A. Initialization File clear all clc n=11; m=0; Nb_days=11; load data.m x=data(); m=x; y=0; save_data=eye(n,Nb_days+1)*0; x_new=max(y);

B. Main File

%Main init;

%Selecting driver data % 1 2 3 4 5 6 % Steady 60 Steady 45 50_70 UDDS HWFET HWFET_2cycles cycle_data_value=[4 4 4 4 4];

Stop_time_value=[1372 1372 1372 1372 1372]; driver_behavior=[1 1 1 1 1];

MPG=0; Cost=0; pred_array=0; zz=0; for j=1:Nb_days cycle_data=cycle_data_value(j); Stop_time=Stop_time_value(j); driver_data=driver_behavior(j); Prediction; Ed(j)=dist(end); Eg(j)=f(end); Es(j)=SOC(end); [FE C]=Fueleconomy(Ed(j),Eg(j),Es(j)); MPG(j)=FE; Cost(j)=C;

146

pred_array(j)=pred; save_data(:,j)=x; figure plot(t,SOC); grid on xlabel('Time') ylabel('SOC (%)'); title(['SOC vs time: Day:',int2str(j+11)]); end for i=1:(n-1) x(i)=x(i+1); end x(n)=max(y); save_data(:,Nb_days+1)=x;

%Plotting data figure for i=1:n bar(i,x(i),'y'); hold on end hold on plot(d,'--r','Linewidth',2); hold on plot(n,pred,'*','Markersize',10); hold on % plot(mean,'--b','Linewidth',2); k=0; z=0; zz(j)=mean; k=n; if (j>1) for i=j:-1:2 z(k)=zz(i); k=k-1; end for l=k:-1:1 z(l)=zz(1); end elseif (j==1) for i=1:n z(i)=mean; end end plot(z,'--b','Linewidth',2); hold on h=legend('Actual Energy' ,3); set(h,'interpreter','none'); xlabel('Last 11 days'); ylabel('Energy usage in kWhr (x)'); 147 title('Usage and prediction graph'); grid on

figure plot(MPG); grid on xlabel('Last 12 days'); ylabel('Fuel economy (mpg)'); title('Fuel economy graph with adaptation'); figure plot(pred_array); grid on hold on plot(save_data(:,j+1))

C. Prediction Algorithm Based on Moving Average

%Prediction for i=1:(n-1) x(i)=x(i+1); %FIFO end x(n)=max(y); sum=0; es=0; c=0; e=0; f=0; for i=1:n sum=sum+x(i); end mean=sum/n;

% Calculating the deviation from the mean for i=1:n d(i)=x(i)-mean; end c=0; es=0; deltaD=0.1*mean; % If the last day data > (10% of mean) if (d(n)>deltaD) es=x(n); for i=n-1:-1:n-3 if ((x(i)>=(-0.1*x(n))) && (x(i)<=(0.1*x(n)))) c=c+1; es=es+x(i); end end if (c>2) 148

pred=(es/4)+0.01; else pred=mean; end end

% If the last day data = (10% of mean <= x(n) => 10% of mean) if((d(n)>=-deltaD) && (d(n)<=deltaD)) pred=x(n); end

% If the last day data < (10% of mean) if (d(n)<-deltaD) es=x(n); for i=n-1:-1:n-3 if ((x(i)>=(-0.1*x(n))) && (x(i)<=(0.1*x(n)))) c=c+1; es=es+x(i); end end if (c>2) pred=(es/4)+0.01; else pred=mean; end end clear c; clear f; clear e; clear es; k=0; z=0; zz(j)=mean; k=n; if (j>1) for i=j:-1:2 z(k)=zz(i); k=k-1; end for l=k:-1:1 z(l)=zz(1); end elseif (j==1) for i=1:n z(i)=mean; end end

%Load data and start simulation t=0; 149 open('Plugin_series_Chevy_adaptation_RV10.mdl'); while (t<60) t=t+1; end; clear t; set_param('Plugin_series_Chevy_adaptation_RV10/Actual','energy','simulated_energy_usage'); set_param('Plugin_series_Chevy_adaptation_RV10','StartTime','0'); set_param('Plugin_series_Chevy_adaptation_RV10','StopTime','Stop_time'); sim('Plugin_series_Chevy_adaptation_RV10');

D. Prediction Algorithm Based on Exception

%Prediction Algorithm based on exception %Created by: Soumendu Chanda %Last Modified: 18th March, 08 sum=0; es=0; c=0; e=0; f=0; ctr=0; ctr1=0; loop=0; for i=1:n sum=sum+x(i); end Xavg_n=sum/n; deltaD=0.1*Xavg_n; deltaM=0.1*x_new; if (x_new~=0)

% Calculating the deviation from the mean d_new=x_new-Xavg_n; d(j)=d_new;

% If the last day data > (10% of mean)

if (abs(d_new)> deltaD)

if(abs(x_new-x(n))<= deltaM && abs(x_new-x(n-1))<= deltaM && abs(x_new-x(n-2))<= deltaM)

pred=(x_new+x(n-2)+x(n-1)+x(n))/4; xx=xx+1;

for i=1:n-1 % Save x_new in main file 150

x(i)=x(i+1); end x(n)=x_new; counter=0;

end

if (abs(x_new-x(n))> deltaM || abs(x_new-x(n-1))>deltaM || abs(x_new-x(n-2))> deltaM)

if (counter==3) ctr=1;

if(abs(x_new-exception(1))<= deltaM) && abs(x_new-exception(2))<= deltaM && (abs(x_new-exception(3))<= deltaM)

pred= (x_new+exception(1)+exception(2)+exception(3))/4; xx=xx+1;

for loop=1:4 for i=1:n-1 x(i)=x(i+1); end end

x(n-3)=exception(1); %Save x_new and exceptions in main file x(n-2)=exception(2); x(n-1)=exception(3); x(n)=x_new; exception(1)=0; exception(2)=0; exception(3)=0; counter=0; k1=1;

elseif ((abs(x_new-exception(1))> deltaM) || abs(x_new-exception(2))> deltaM || (abs(x_new- exception(3))>deltaM)&&(yy>2*xx))

for loop=1:4 for i=1:n-1 x(i)=x(i+1); end end

x(n-3)=exception(1); %Save x_new and exceptions in main file x(n-2)=exception(2); x(n-1)=exception(3); x(n)=x_new; exception(1)=0; exception(2)=0; exception(3)=0; counter=0; k1=1; 151

xx=0; yy=0; pred=Xavg_n;

elseif ((abs(x_new-exception(1))>deltaM) || abs(x_new-exception(2))> deltaM || (abs(x_new- exception(3))>deltaM)&&(yy <=2*xx))

exception(1)=exception(2); exception(2)=exception(3); exception(3)=x_new; pred=Xavg_n; yy=yy+1;

end

if ((counter<3)&& (ctr==0))

counter=counter+1;

if (k1>3) ctr1=1; if (yy>2*xx)

for loop=1:4 for i=1:n-1 x(i)=x(i+1); end end

x(n-3)=exception(1); %Save x_new and exceptions in main file x(n-2)=exception(2); x(n-1)=exception(3); x(n)=x_new; exception(1)=0; exception(2)=0; exception(3)=0; counter=0; k1=1; xx=0; yy=0; pred=Xavg_n;

else exception(1)=exception(2); exception(2)=exception(3); exception(3)=x_new; pred=Xavg_n; yy=yy+1; end end

152

if ((k1<=3) && (ctr1==0)) exception(k1)=x_new; k1=k1+1; pred=Xavg_n; yy=yy+1;

end end

end

end % If the last day data = (-10% of mean <= x(n) =>10% of mean)

if(abs(d_new)

pred=Xavg_n; for i=1:n-1 %Save x_new in main memory x(i)=x(i+1); end x(n)=x_new; counter=0; xx=xx+1;

end

else pred=Xavg_n; end

clear c; clear f; clear e; clear es;

%Load data and start simulation t=0; open(‘Plugin_series_Chevy_adaptation_RV10.mdl’); while (t<60) t=t+1; end; clear t; set_param(‘Plugin_series_Chevy_adaptation_RV10/Actual’,’energy’,’simulated_energy_usage’); set_param(‘Plugin_series_Chevy_adaptation_RV10’,’StartTime’,’0’); set_param(‘Plugin_series_Chevy_adaptation_RV10’,’StopTime’,’Stop_time’); sim(‘Plugin_series_Chevy_adaptation_RV10’); 153

APPENDIX D

ENERGY MANAGEMENT ALGORITHM

SERIES PHEV

6 [SOC_battery] SOC_battery MATLAB [cycle_complete] Function

4 -K- SOC_lower_limit_old 7 1 1 [B] -K- [C] Whr_predicted Energy required 1 Energy av ailable 5 Gain Total AHr Gain3 Vnom 2 Whr [B]

<= Cy cle OR [C]

== 1 [B] > 0 OR 2 SOC_lower_limit_new Saturation [cycle_complete] == 0 0 3

0 Engine_turn_off Switch4 [cycle_up] > 7

[B] < 0 AND

90 [cycle_complete] == 1 [B] 0 > [C] 0

[cycle_up] > 7 50 == 1 OR 1 [B] < 0 SOC_upper_limit Saturation1

3 SOC_upper_limit_ old Switch7 [cycle_complete] [SOC_battery] MATLAB [cycle_up] Function

154

SERIES-PARALLEL PHEV

5 [SOC_battery] Enable SOC_battery MATLAB [cycle_complete] Function

6 1 1 [B] Whr_predicted Energy required Gain 2 Whr

3 -K- SOC_lower_limit_old -K- [C] 1 4 Energy av ailable Total AHr Gain3 Vnom 20

Constant == 0 [B]

<= Cy cle OR 7 2 [C] Parallel mode SOC_lower_limit_new Saturation == 1 3 AND Engine_turn_off [B] > 0 OR

[cycle_complete] == 0 0

Switch4 [cycle_up] > 7

[B] < 0 AND

90 [cycle_complete] == 1 0 [B] > [C] 0

[cycle_up] > 7

== 1 OR 1 [B] < 0 SOC_upper_limit Saturation1

100

Switch7

4 [cycle_complete] Cycle_up [SOC_battery] MATLAB [cycle_up] Function

155