EFFICIENCY ANALYSIS OF OPTIMIZED HEV AGAINST CONVENTIONAL VEHICLES, IN A SRI LANKAN DRIVE CYCLE A dissertation submitted to the Department of Electrical Engineering, University of Moratuwa In partial fulfillment of the requirements for the Degree of Master of Science By I. W. D. R. S. KARUNARATNE L I B R A R Y ITYC • • • •^hu las i ;* MORA . <'WA Supervised by Dr. Lanka Udawatta University of Moratuwa 92964 S 2 \ 3 * Tva - Department of Electrical Engineering University of Moratuwa * » v January 2009 DECLARATION The work submitted in this dissertation is the result of my own investigation, except where otherwise stated. It has not already been accepted for any degree, and is also not being concurrently submitted for any other degree. Date: 5 o / o i A © 0 3 We/I endorse the declaration by the candidate. 11 CONTENTS Declaration Abstract Dedication Acknowledgement List of Figures List of Tables 1. Introduction 1.1 Hybrid Electric Vehicles 1.1.1 Fuel Consumption 1.1.2 Noise 1.1.3 Pollution 1.2 Literature Review 1.3 Objective 2 HEV Classification 2.1 Parellel HEVs 2.2 Series HEVs 2.3 Parallel- Series (Duel)HEVs 3 Drive Cycles 3.1 Drive cycle classification 3.2 Standard drive cycles 3.3 Colombo Drive Cycle(CDC) 3.4 Development methodology 3.4.1 Route 3.4.2 Data Collection iii 3.4.3 GPS Performances 18 3.4.4 Data Collection Protocol 19 3.5 Vehicle Parameters 21. 3.6 Developed CDC 23 4 HEV Simulation 33 4.1 Factors of modeling tools 33 4.2 Tools for vehicle modeling 34 4.3 Modeling Types 35 4.4 Models used in the Study 35 4.5 ADVISOR simulation 39 4.5.1 Parallel HEV 40 4.5.2 TOYOTA Prius 41 4.5.3 Conventional vehicle 42 4.6 Vehicle specifications 43 5 HEV performances in CDC 5.1 Results and analysis 46 5.2 Results in detail 49 6 ADVISOR Results 6.1 ADVISOR results analysis 59 6.2 Results in parallel HEV 61 6.3 Results in TOYOTA Prius 68 6.4 Results in conventional vehicle 72 6.5 Conclusion 79 References 80 Appendix A : Published paper Appendix B : Colombo drive cycle data iv ABSTRACT Due to the constant increase of fuel prices and environmental concerns, researchers were pushed to thinking more about fuel-efficiency and reduction of emission on vehicles. As a result there was great enthusiasm for researchers to look into and introduce hybrid technology to the field of automobiles. For example in hybrid electric power trains, an internal combustion engine (ICE) together with an electric motor (EM) is used as two energy sources. The use of an electrical motor in place of the internal combustion engine during different stages of driving resulted in a definite saving in fuel consumption. In this study, a conventional vehicle and a HEV with varying traffic conditions & flow were compared in relation to fuel economy. The main aspect was to compare & evaluate HEV and conventional vehicles in the Sri Lankan environment. With that in mind, developing a drive cycle in the Sri Lankan environment was essential. The Colombo drive cycle (CDC) was developed to fulfill that aspect using GPS protocol. The HEV and conventional vehicles were simulated in following models using Colombo drive cycle. • Parallel HEV • Series HEV • Conventional vehicle with CVT • TOYOTA Prius Simulation Models developed in MATLAB was used and to verify that QSS TB simulation model and ADVISOR simulation software was adapted. Results showed that, with Colombo drive cycle, the two extremes come with maximum efficiency model and conventional vehicle. It proves that the optimized Parallel HEV with future data gives far better fuel economy in a real world drive cycle like CDC. Optimized HEV with prediction is so efficient in drive cycles which has so many sudden changes in acceleration, decelerating, cruse control and idle during the drive. Results were proven by comparison with simulating of above models and other available standard drive cycles. The optimized TOYOTA Prius performed far superior in the current HEV market. It 's performance was excellent especially in vulnerable drive conditions. v DEDICATION I dedicate this dissertation to my loving parents. ACKNOWLEDGEMENT Firstly, I wish to thank Dr. Lanka Udawatta for guiding me in this research and helping me to complete it within the given time frame. As the Research Supervisor, he directed me in finding all the necessary literature and to research the work to a high standard. Secondly, a very big thank you to both Prof. Saman Halgamuge and Mr.Sunil Adikari, School of Engineering, University of Melbourne, Australia for providing the necessary research materials and information of HEVs required for this study. Thirdly, I thank all the lectures of Electrical and Mechanical Engineering Departments of University of Moratuwa, who participated in the progress review presentation. Due to their invaluable comments which helped me to achieve the goal of completing this research study. I would be failing in my duty if I do not convey my sincere thanks to my two colleagues Mr. Sudath Wimalendra and Mr. Chaminda Edirisinghe. These two batch mates encouraged me from the very beginning to successfully complete the work to the very end. My heartfelt thanks go to my Parents, Brother and Sister and my wife for their love, understanding and encouragement throughout this study. Last but not least, I wish to thank all those numerous persons who are too many to mention and in their small way gave me great support to complete this thesis. 11 LIST OF FIGURES Figure Description Page Figure 1.1 Hybrid car sales 02 Figure 2.1: Block diagram of Pre- transmission parallel HEV 07 Figure 2. 2: Block diagram of Post-transmission parallel HEV 08 Figure 2. 3: Block diagram of all wheel drive parallel HEV 08 Figure 2.4: Block diagram of Series HEV 09 Figure 3 . 1 : Standard Model drive cycles 11 Figure 3.2: Standard Transient drive cycles 13 Figure 3.3: CDC Route 2 16 Figure 3.4: CDC Route 1 17 Figure 3.5: GPS Receiver used 18 Figure 3.6: Vehicle used for data collection 21 Figure 3.7: Route 1 Up drive Speed profile 23 Figure 3.8: Route 1 Up drive Acceleration profile 24 Figure 3.9: Route 1 Up drive in ADVISOR 24 Figure 3.10: Route 1 Down drive Speed profile 25 Figure 3.11: Route 1 Down drive Acceleration profile 26 Figure 3.12: Route 1 Down drive in ADVISOR 26 Figure 3.13: Route 2 Up drive Speed profile 27 Figure 3.14: Route 2 Up drive Acceleration profile 28 Figure 3.15: Route 2 Up drive in ADVISOR 28 Figure 3.16: Route 2 Down drive Speed profile 29 Figure 3.17: Route 2 Down drive Acceleration profile 30 Figure 3.18: Route 2 Down in ADVISOR 30 Figure 3.19: Route 1 Up drive Distance 31 Figure 3.20: Route 1 Down drive Distance 31 Figure 3.21: Route 1 Up drive Distance 32 Figure 3.22: Route 1 Down drive Distance 32 Figure 4. 1: QSS TB Block diagram for Series HEV 37 in Figure 4. 2: QSS_TB Block diagram for Conventional vehicle 38 Figure 4.3: Block diagram for parallel HEV in ADVISOR 40 Figure 4.4: Parameter setting for parallel HEV in ADVISOR 40 Figure 4.5: Block diagram for Toyota Prius in ADVISOR 41 Figure 4.6: Parameter setting for Toyota Prius in ADVISOR 41 Figure 4.7: Block diagram for conventional vehicle in A D V I S O R 42 Figure 4.8: Block diagram for conventional vehicle in ADVISOR 42 Figure 4.9: Fuel consumption map of the ICE 44 Figure 4.10: Engine fuel efficiency contour 45 Figure 5.1: Analysis of QSS_TB Model results 48 Figure 5.2: Results of Series HEV in CDC 1U 49 Figure 5.3: Results of Series HEV in CDC ID 50 Figure 5.4: Results of Series HEV in CDC 2U 51 Figure 5.5: Results of Series HEV in CDC 2D 52 Figure 5.6: Results of Series HEV in NEDC 53 Figure 5.7: Results of Series HEV in FTP Highway 54 Figure 5.8: Results of Series HEV in Japan 10-15 55 Figure 5.9: Results of Conventional vehicle in CDC 1U 56 Figure 5.10: Results of Conventional vehicle in CDC ID 56 Figure 5.11: Results of Conventional vehicle in CDC 2U 56 Figure 5.12: Results of Conventional vehicle in CDC 2D 57 Figure 5.13: Results of Conventional vehicle in NEDC 57 Figure 5.14: Results of Conventional vehicle in FTP Highway 57 Figure 5.15: Results of Conventional vehicle in Japan 10-15 58 Figure 6.1: Graph for fuel economy comparison 60 Figure 6.2: Results of parallel HEV in CDC 1U 61 Figure 6.3: Motor efficiency of parallel HEV in CDC 1U 61 Figure 6.4: Results of parallel HEV in CDC 1D 62 Figure 6.5: Motor efficiency of parallel HEV in CDC ID 62 Figure 6.6: Results of parallel HEV in CDC 2U 63 Figure 6.7: Motor efficiency of parallel HEV in CDC 2U 63 Figure 6.8: Results of parallel HEV in CDC 2D 64 Figure 6.9: Motor efficiency of parallel HEV in CDC 2D 64 Figure 6.10: Results of parallel HEV in NEDC 65 IV Figure 6.11: Motor efficiency of parallel HEV in NEDC 65 Figure 6.12: Results of parallel HEV in Japan 10-15 66 Figure 6.13: Motor efficiency of parallel HEV in Japan 10-15 66 Figure 6.14: Results of parallel HEV in US 06 67 Figure 6.15: Motor efficiency of parallel HEV in US 06 67 Figure 6.16: Results of Toyota Prius in CDC 1U 68 Figure 6.17: Results of Toyota Prius in CDC ID 68 Figure 6.18: Results of Toyota Prius in CDC 2U 69 Figure 6.19: Results of Toyota Prius in CDC 2D 69 Figure 6.20: Results of Toyota Prius in NEDC 70 Figure 6.21: Results of Toyota Prius in Japan 10-15 70 Figure 6.22: Results of Toyota Prius in US 06 71 Figure 6.23: Results of Conventional vehicle in CDC 1U 72 Figure 6.24: Motor efficiency of Conventional vehicle in CDC 1U 72 Figure 6.25: Results of Conventional vehicle in CDC ID 73 Figure 6.26: Motor efficiency of Conventional vehicle in CDC ID 73 Figure 6.27: Results of Conventional vehicle in CDC 2U 74 Figure 6.28: Motor efficiency of Conventional vehicle in CDC 2U 74 Figure 6.29: Results of Conventional vehicle in CDC 2D 75 Figure 6.30: Motor efficiency of Conventional vehicle in CDC 2D 75 Figure 6.31: Results of Conventional vehicle in NEDC 76 Figure 6.32: Motor efficiency of Conventional vehicle in N E D C 76 Figure 6.33: Results of Conventional vehicle in Japan 10-15 77 Figure 6.34: Motor efficiency of Conventional vehicle in Japan 10-15 77 Figure 6.35: Results of Conventional vehicle in US 06 78 Figure 6.36: Motor efficiency of Conventional vehicle in US 06 78 v LIST OF TABLES Table Description Page Table 3.1: Colombo drive cycle details 14 Table 3.2: Vehicle model specifications 19 Table 4.1: Available modeling tools 32 Table 4.2: Vehicle model specification 41 Table 5.1: QSS_TB Results 44 Table 5.2: Economy Analysis 45 Table 5.3: Analysis of CDC 46 Table 6.1:ADVISOR Results 57 vi CHAPTER 01 Introduction A hybrid vehicle is an automobile that has two or more major sources of propulsion power. Most hybrid cars currently marketed to consumers have both conventional gasoline and electric motors, with the ability to power the vehicle by either one independently or in tandem. Those are called as Hybrid Electric vehicles (HEV) Other power sources may include hydrogen, propane, CNG, and solar energy. The technology used depends on the utilization of the vehicle, whether it should be fuel efficient, powerful , driving range, or reduced greenhouse gas emissions. The HEVs currently available in the market till today are usually developed for reduced emissions and driving range. Corporate and government fleets that have been in service for twenty years or more are usually tuned for fuel efficiency, often at the cost of driving range, power, and hydrocarbon emissions. Newly produced hybrid electric vehicles prolong the charge on their batteries by capturing kinetic energy via regenerative braking, and some HEVs can use the internal combustion engine (ICE) to generate electricity by spinning motor-generator to either recharge the battery or directly feed power to an electric motor that drives the vehicle. Many HEVs reduce idle emissions by shutting down the ICE at idle and restarting it when needed (start-stop system). An H E V s engine is smaller than a non-hybrid petroleum fuel vehicle and may be run at various speeds, providing more efficiency. HEVs became widely available to the public in the late 1990s with the introduction of the Honda Insight and Toyota Prius. HEVs are viewed by some automakers as a core segment of the future automotive market.' Automotive hybrid technology became successful in the 1990s when the Honda Insight and Toyota Prius became available. These vehicles have a direct linkage from the ICE to the driven wheels, so the engine can provide acceleration power. 1 The Prius has been in high demand since 2004. Newer designs have more conventional appearance and are less expensive, often appearing and performing identically to their non-hybrid counterparts while delivering 40% better fuel efficiency. The Honda Civic Hybrid appears identical to the non-hybrid version, for instance, but delivers about 50 miles per US gallon. The redesigned 2004 Toyota Prius improved passenger room, cargo area, and power output, while increasing energy efficiency and reducing emissions. The Honda Insight, while not matching the demand of the Prius, stopped being produced after 2006 and has a devoted base of owners. According to many marketing researches the prediction for sales of hybrids is increasing yearly. Growth in Hybrid market Year Figure 1.1 Hybrid car sales The black line shows hybrid sales continuing at their current pace since hybrids were introduced in year 2000. 1.1 Hybrid Electric Vehicles 1.1.1 Fuel consumption Hybrid vehicles are the best bet to get the most out of each tank of fuel during city driving Current HEVs reduce petroleum consumption under certain circumstances, 2 compared to otherwise similar conventional vehicles, primarily by using three mechanisms 1. Reducing wasted energy during idle/low output, generally by turning the ICE off 2. Recapturing waste energy (i.e. regenerative braking) 3. Reducing the size and power of the ICE, and hence inefficiencies from under- utilization, by using the added power from the electric motor to compensate for the loss in peak power output from the smaller ICE. Any combination of these three primary hybrid advantages may be used in different vehicles to realize different fuel usage, power, emissions, weight and cost profiles. The ICE in an HEV can be smaller, lighter, and more efficient than the one in a conventional vehicle, because the combustion engine can be sized for slightly above average power demand rather than peak power demand. The drive system in a vehicle is required to operate over a range of speed and power, but an ICE's highest efficiency is in a narrow range of operation, making conventional vehicles inefficient. On the contrary, in most HEV designs, the ICE operates closer to its range of highest efficiency more frequently. The power curve of electric motors is better suited to variable speeds and can provide substantially greater torque at low speeds compared with internal-combustion engines. The greater fuel economy of HEVs has implication for reduced petroleum consumption and vehicle air pollution emissions worldwide. 1.1.2 Noise Reduced noise emissions resulting from substantial use of the electric motor at idling and low speeds, leading to roadway noise reduction.' in comparison to conventional gasoline or diesel powered engine vehicles, resulting in beneficial noise health effects (although road noise from tires and wind, the loudest noises at highway speeds from the interior of most vehicles, are not affected by the hybrid design alone). Reduced noise may not be considered an advantage by some; for example, some people who are blind or visually-impaired consider the noise of combustion engines a helpful aid while crossing streets and feel quiet hybrids could pose an unexpected hazard. 3 1.1.3 Pollution Reduced air pollution emissions, due to lower fuel consumption, lead improved human health with regard to respiratory problems and other illnesses. Better fuel efficiency means less use of fossil fuels and the energy and pollution associated with refining them, while lower emissions mean less air pollution from exhaust. However, better fuel efficiency does not necessarily go hand-in-hand with lower emissions. Pollution reduction in urban environments may be particularly significant due to elimination of idle-at-rest Battery toxicity is a concern, although today's hybrids use NiMH batteries, not the environmentally problematic rechargeable nickel cadmium. "Nickel metal hydride batteries are benign. They can be fully recycled," says Ron Cogan, editor of the Green Car Journal. Toyota and Honda say that they will recycle dead batteries and that disposal will pose no toxic hazards. Toyota puts a phone number on each battery, and they pay a $200 "bounty" for each battery to help ensure that it will be properly recycled. This is considered as a major advancement that came with HEVs and it used as a marketing tool effectively. 4 1.2 Literature review HEV is a revolutionary concept to the modern world and carries great potential for the future. Therefore it has been able to stimulate the world of engineers ' researchers and inventors. It is inevitable to stop them going after this revolution. HEV concept impacts and the benefits of HEVs are discussed and compared with those of conventional vehicles in[2] Current status, design and selection of HEV components are presented in [2] [3] [4] Due to the complex nature of hybrid electric vehicles, control strategies are of high interest. Extensive studies have been conducted over the past two decades. In particular, several logic-based control strategies and fuzzy logic-based energy management strategies for distributing power demand have been suggested [1] [12] Recent changes in the technology of modern vehicles and revolutionary development in telematics industry have created the possibility for a vehicle to gather online information about the road infrastructure and the traffic environment in which it is in operation. This information can be used by the HEV power management system to predict its future speed trend in order to decide optimum power split between the two sources more effectively. Several algorithms have been proposed to predict the future speed trends, with the use of preview information provided by the telematics. A methodology of combining two technologies, hybrid and telematics together to create "intelligent vehicle" which provides improved fuel economy with traffic preview is discussed in[ 15] Most of these concepts are in a developing stage and there is room for more developments. Therefore vehicle simulation is playing a major role in this scenario. For vehicle simulation and modeling drive cycles are a very important aspect. All models are tested and certified by simulating in drive cycle. Study of drive cycles and learning about developing drive cycles is a very important aspect as well in vehicle modeling and simulation [15] 5 1.3 Objective The main objective of this study is to develop a drive cycle on Sri Lankan road- ways which have frequent variable road conditions. This will be referred to as Colombo Drive Cycle (CDC). When developing or evaluating vehicle performance, most developers use existing drive cycles in US, Europe or Japan. Therefore we cannot expect the same performance when driving those vehicles in Sri Lankan context. This developed drive cycle gives the opportunity to researchers to simulate their vehicles in such conditions. Secondary objective is to simulate following models with above mentioned developed drive cycle. • Parallel HEV with maximum efficiency can be achieved • Optimized PHEV with future predictions • Series HEV • Conventional vehicle with CVT • Conventional vehicle The efficiency of above vehicle models will be analyzed in terms of fuel economy. All simulations were done in MATLAB / Simulink software , QSS toolbar and ADVISOR software also used in developing the models. Thereafter these models were compared between NEDC, Japan 10-15, US 06 drive cycles and CDC for analysis and efficiency was analyzed on the basis of fuel economy. Behavior of those models in CDC is analyzed and compared at the end. 6 CHAPTER 2 HEV Classifications There are many ways to classify HEVs. One of the most common ways to classify HEV is based on configuration of the vehicle drive train. Based on this, three major hybrid vehicle architectures introduced are parallel, series and series-parallel. 2.1 Parallel HEVs In parallel configurations, both the engine and the motor provide traction power to the wheels, which means that the hybrid power is summed at a mechanical node to power the vehicle. As a result, both of the engine and the motors can be downsized, making the parallel architecture more viable with lower costs and higher efficiency [11],[12]. The parallel HEVs usually use the same gearboxes of the counterpart conventional vehicles, either in automatic or manual transmissions. Based on where the gearbox is introduced in the powertrain, there are two typical parallel HEV architectures, named pre-transmission parallel and post-transmission parallel, as shown in Figure 2. 1 and Figure 2. 2, respectively. In a pre-transmission parallel HEV, the gearbox is located on the main drive shaft after the torque coupler. Hence, gear speed ratios apply on both the engine and the electric motor. The power flow is summed at the gearbox. On the other hand, in a post-transmission parallel hybrid, the gearbox is located on the engine shaft prior to the torque coupler. The gearbox speed ratios only apply on the engine. Torque £ Figure 2.1: Block Diagram of Pre - Transmission Parallel HEV 7 Torque Figure 2. 2 : Block Diagram of Post - Transmission Parallel HEV In a pre-transmission configuration, torque from the motor is added to the torque f rom the engine at the input shaft of the gearbox. In a post-transmission, the torque from the motor is added to the torque from the engine delivered on the output shaft of the gearbox. A disconnect device such as a clutch is used to disengage the gearbox while running the motor independently. There are attempts from different perspectives to improve the operation of a parallel HEV. One possibility is to run the vehicle on electric machine alone in city driving while running engine power alone on highways. Most contemporary parallel vehicles use a complex control system and special algorithms to optimize both vehicle performance and range. One unique implementation of the parallel hybrid technology is on an all wheel drive vehicle as shown in Figure 2. 3. The design is most beneficial if the ICE powers the rear wheels while the electric motor powers the front wheels. The more weight borne by the front wheels during braking will result in more power captured during regenerative braking. The design is also effective on slippery surfaces by providing vehicle longitudinal stability control that is not as easy with other types of hybrid designs. The power to each axle is manipulated by a single controller, although this requires a fast data communication. Figure 2. 3 : Block Diagram of all wheel drive Parallel H E V 8 The flexibility in power train design, in addition to the elimination of the need for a large motor, of parallel hybrids has attracted more interest in HEV development than the series hybrids. 2.2 Series HEVs One of the basic types of HEV is series hybrid. In this configuration, as shown in Figure 2.4, the ICE is used to generate electricity in a generator. Electric power produced by the generator goes to either the motor or Battery. The hybrid power is summed at an electrical node, the motor. Despite the early research and prototypes, the possibility for series hybrids to be commonly used in vehicular applications seems to be remote. The series hybrid configuration tends to have a high efficiency at its engine operation. However, the summed electrical mode has tied up the size of every component. The weight and cost of the vehicle is increased due to the large size of the engine and the two electric machines needed. The size of the power electronic unit is also excessive. Figure 2.4 : Block Diagram Series HEV 2.3 Parallel - Series ( D u a l ) HEVs This system combines the series hybrid system with the parallel hybrid system in order to maximize the benefits of both systems [11],[12], In the series-parallel configurations, the vehicle can operate as a series hybrid, a parallel hybrid, or a combination of both. This design depends on the presence of two motors/generators and the connections between them, which can be both electrical and mechanical. One advantage of a series-parallel configuration is that the engine speed can be decoupled from the vehicle speed. This advantage is partially offset by the additional losses in the conversion between mechanical power from engine and electrical energy. 9 CHAPTER 3 Drive cycle A 'Drive cycle' can be expressed as a series of data gathered during a vehicle drive. The data is completed with Speed of that vehicle verses time. Why a drive cycle? The answer to this question is this speed vs. time profile can be used to evaluate a vehicles behavior especially in the process of vehicle modeling and simulation for various aspects such as energy efficiency, fuel consumption, stability certification testing. 3.1 Drive cycle Classification There are many developed drive cycles to utilize in above purposes. Basically we can categorize drive cycles in to two main categories. • Transient Drive cycle • Model drive cycles Transient drive cycles Test drive cycles are derived by collecting actual data in the real world. Those are very realistic and taken using a real vehicle in actual conditions and data recorded in a live environment with actual disturbances. Those drive cycles are considered as very effective when using for simulation of certification activities. Most of the US based drive cycles are transient drive cycles. Model drive cycles Model drive cycles are the cycles which derived by mathematical modeling with the help of statistics. In those drive cycles they have included some conditions where it is difficult to achieve in real world such as maximum speed and operate in a constant speed over time duration. Most of the European standard drive cycles and Japanese drive cycles are belongs to this category 10 3.2 Available standard drive cycles Model drive cycles NEDC FUDS ECE 15 EUDC JAPAN lOMODE JAPAN 15 M O D E JAPAN 10-15 M O D 15 Mode N E D C 10 Mode 2 0 0 d O O SOO 8 0 0 1 0 0 0 t(S| FTP 72/UDDC/FUDSJLA 4 1 2 0 0 1 4 0 0 1 5 0 0 E U D C E C E 15 10-15 Mode Figure 3. 1 Standard Model drive cycles 11 HYZEM Highway IM 240 3 0 0 HYZEM Road LA 92 120 .0 Figure 3.2 Standard Transient Drive cycles 13 120.0 100.0 • 80 0 • 60 0 • 40.0 • 20 0 • 0.0 I 0 100.0 i 8 0 . 0 • . c | eo.o • > 40 0 - 20.0 1 3.3 Colombo Drive Cycle Last chapter was all about a description of existing and available drive cycles being used in various situations. But if we analyze them further, we can observe that all those drive cycles are developed in Europe, US and Japan. 1 was unable to find a drive cycle which was developed in the Asian region. I found that it is very important in developing a drive cycle in such a country as those countries are representing large market share for vehicles in contrasting road characteristics. Therefore it is very important to simulate vehicles in those conditions also, to analyze the vehicle behavior. The most affecting factor is the road conditions. In Sri Lanka the road conditions are not in a satisfactory condition compared with US or EU. As a developing country it is difficult to maintain existing roads as in those countries, mainly due to lack of funds. Road condition: As a tropical country the monsoon rainfall is high in Sri Lanka. Poor drainage vis-a- vis flooding of roads is the main reason road surfaces are being damaged frequently. Sub standard construction and poor quality material used and half completion are the main reasons for damage to roads. A badly constructed road, mainly by local contractors, causes severe congestion & traffic blocks thereby not only reduce vehicular movement but a heavy toll on the vehicle itself. High traffic: In Sri Lanka almost all the large city roads are congested with traffic blocks! The most critical times are early morning and evenings. Morning hours between 6.30 a.m. & 9.00 a.m. are much worse especially because of people traveling to schools and/or offices. The same occurs in the afternoon at 1.30. - 3.00 p.m. when Schools close and at 5.00 p.m. and 7.30 p.m. when most offices close. High vehicle density: In urban areas too the numbers of vehicles traveling along the roads are very high. All modes of transport such as Busses, Motor cycles, Trishaws, Lorries, Vans use the 14 same roads, and there is no discipline on the roads like in other countries where there are strict lanes for use by each category of vehicle. Indiscipline driving behaviors: Unfortunately in Sri Lanka there is a total lack of discipline by all motorists and pedestrians alike! We observe various degrees of reckless driving by especially Buss & Trishaw drivers. Most of them are very undisciplined and aggressive! However on the good side is that motorists are very careful nowadays with the introduction of the breathalyzer by the Police to stop drunken drivers of all categories. Speed limits: Normally within the urban areas the speed limit is 50 km/h. and on certain roads out station it is on average. 60 -70km/h. Speed limit within most Cities are restricted to 40km/h. Many unexpected obstacles: There are many unexpected obstacles when driving on roads in Sri Lanka. People jay walking on the roads and the crossing the road at non pedestrian crossing locations, Road constructions works are frequent with no proper warning to motorists. Cattle, dogs, goats and others animals crossing the roads due to unprotected road boundaries. Buss drivers not adhering to stopping at regulated bus stops, causing traffic blocks. Narrowness of roads in the country which have not been widened and un graded for several decades. 3.4 Developing Methodology When developing the drive cycle following are considered as the main aspects. • Route • Data collection. 3.4.1 Route When developing Colombo drive cycle I selected two routes. First one is Maharagama to Kirulapona and second one is Kirulapona to Dematagoda(Base line). Those two routes are very different from each other. 15 First one is going through a road which is condition is not good. Also there are so many traffic situations and so many unexpected stoppages. Most of places only one track can be used to one direction. Figure 3.3 Comparatively second Route is one of the best roads we can find in Colombo. Road condition is good. Tarred well and carpet finish with colas. Comparatively to route 1 less traffic, except in traffic lights. Also two tracks are available from beginning to end for one direction. Figure 3.4 Table 3. 1 Colombo Drive Cycle Details Route 1 Route 2 Terminating points Maharagama- Kirulapona Kirulopona to Dematagoda Up CDC1U Down CDC ID Up CDC2U Down CDC 2D Distance 5900m 5150m 6500m 6480m Time Duration 903 s 845 s 783 s 836 s No of traffic lights Figure 3.3 CDC Route 2 16 Figure 3.4 CDC Route 1 3.4.2 Data Collection The Global Positioning System (GPS) protocol was the preferred method to use in data collection. A GPS unit was fixed to the roof of the vehicle and the laptop computer was connected to the unit. Sample time : 2 Sec Antenna : External antenna connected to roof top of the vehicle. During the road rips all data was collected to the computer for analysis. The hardware interface for GPS units is designed to meet the N M E A requirements. This is compatible with serial ports using RS232 protocols. 17 3.4.3 GPS Performance GARMIN GPS 76 was the GPS receiver used for data collection. Following are the specification of the unit. Receiver: WAAS enabled, 12 parallel channel GPS receiver continuously tracks and uses up to 12 satellites to compute and update your position Acquisition times: Warm: Approximately 15 seconds Cold: Approximately 45 seconds AutoLocate™: Approximately 5 minutes Update rate: 1/second, continuous GPS accuracy: Position: < 15 meters, 95% typical* Velocity: 0.05 meter/sec steady state ViES!?' Figure 3.5 GPS Receiver Used DGPS (USCG) accuracy: Position: 3-5 meters, 95% typical Velocity: 0.05 meter/sec steady state DGPS (WAAS) accuracy: Position: < 3 meters, 95% typical Velocity: 0.05 meter/sec steady state Dynamics: 6 g ' s 18 .iHcRSITY u. . LAiaA , m o r a t u w a Interfaces: RS232 with N M E A 0183, RTCM 104 DGPS data format and proprietary GARMIN Antenna: Built-in quadrifilar, with external antenna connection (MCX) 3.4.4 Data Collection Protocol The National Marine Electronics Association (NMEA) has developed a specification that defines the interface between various pieces of marine electronic equipment. The standard permits marine electronics to send information to computers and to other marine equipment. GPS receiver communication is defined within this specification. Most computer programs that provide real time position information understand and expect data to be in NMEA format. This data includes the complete PVT (position, velocity, time) solution computed by the GPS receiver. The idea of N M E A is to send a line of data called a sentence that is totally self contained and independent from other sentences. There are standard sentences for each device category and there is also the ability to define proprietary sentences for use by the individual company. All of the standard sentences have a two letter prefix that defines the device that uses that sentence type. (For gps receivers the prefix is GP.) which is followed by a three letter sequence that defines the sentence contents. In addition NMEA permits hardware manufactures to define their own proprietary sentences for whatever purpose they see fit. All proprietary sentences begin with the letter P and are followed with 3 letters that identifies the manufacturer controlling that sentence. For Garmin sentence is start with PGRM. Each sentence begins with a "$' and ends with a carriage return/line feed sequence and can be no longer than 80 characters of visible text (plus the line terminators). The data is contained within this single line with data items separated by commas. The data itself is just ascii text and may extend over multiple sentences in certain specialized instances but is normally fully contained in one variable length sentence. The data may vary in the amount of precision contained in the message. For example time might be indicated to decimal parts of a second or location may be show with 3 or even 4 digits after the decimal point. Programs that read the data should only use the commas to determine the field boundaries and not depend on column positions. There S 2 C 6 4 19 is a provision for a checksum at the end of each sentence which may or may not be checked by the unit that reads the data. The checksum field consists of a ' * ' and two hex digits representing an 8 bit exclusive OR of all characters between, but not including, the ' $ ' and ' * ' . A checksum is required on some sentences. When considering the hardware connection the N M E A standard is not RS232. They recommend conformance to EIA-422. The N M E A standard for interface speed is 4800 b/s with 8 bits of data, no parity, and one stop bit. All units that support N M E A are supporting this speed. Note that, at a b/s rate of 4800, you can easily send enough data to more than fill a full second of time. The N M E A standard has been around for many years (1983) and has undergone several revisions. The protocol has changed and the number and types of sentences may be different depending on the revision. Most GPS receivers understand the standard which is called: 0183 version 2 Generally the cable is unique to the hardware model and the cable made specifically for the brand and model of the unit. 20 3.5 Vehic le parameters Vehicle specification which used for generating CDC Suzuki Swift Figure 3.6 Vehicle used for data collection Table 3.2: Vehicle specifications GENERAL Body type Hatch Drive FF Transmission 4 speed automatic Displacement, cc l328 Frame LA-HT51S-CSEA-Z3 SPECIFICATION (SPECS) EXTERIOR Exterior dimensions (LxWxH), mm 3615 x 1600 x 1540 Interior dimensions (LxWxH), mm 1695 x 1345 x 1250 Wheel base, mm 2360 Treads (F/R), mm 1405 / 1385 Ground clearance, mm 165 Curb vehicle weight, kg 910 Gross vehicle weight, kg Seating capacity, persons 5 Doors number 5 Min.turning radius, m 4.9 Fuel tank capacity, 1 41 21 ENGINE Displacement, cc l328 Engine modelM13A Max.power (Net), kw(PS)/rpm 88 ps (64/72 kw) / 6000 rpm Max.torque(Net), N*m(kg*m)/rpm 12.0 kg*m (117.68 N*m) / 3400 rpm Power density 10.34 Engine type Water cooling serial 4 cylinder DOHC16 valve Engine information VVT (variable valve timing mechanism) Fuel system EPI (electrically controlled gasoline injection) Turbocharger No Fuel type Unleaded regular gasoline LEV system (Low emission vehicle) Yes Compression ratio 9.5 Bore, mm 78 Stroke, mm 69.5 Fuel consumption at 10-15 modes, 1/100km 5.7 CHASSIS / TRANSMISSION Power steering Yes Tires size, front 165/70R14 81s Tires size rear 165/70R14 81s Braking system, front Ventilated disk Braking system, rear Drum (leading/trailing) Suspension system, front McPherson strut type coil spring Suspension system, rear IT.L. (isolated trailing link) type coil spring 22 3.6 Developed Drive Cycle Route 1 Up Figure 3.7 is to show the speed Vs time for the CDC 1U drive cycle. The maximum speed is 23.57 kmph. Here we can see the speed is always varying in big margins through out the time scale. Figure 3.8 is to show the acceleration profile for the CDC 1U drive cycle. The maximum acceleration is 1.67m/s"2 The maximum deceleration is -1.75 m/s . Fig 3.9 is the summery taken from the ADVISOR software after feeding all the relevant data collected during developing Colombo drive cycle. In this screen it provides all the relevant and valuable statistics of this drive cycle. 14 12 10 J2 8 E " O CD 0) ^ 6 co D 4 2 0 0 100 200 300 400 500 600 700 800 900 1000 Time (Sec) Figure 3.10 Route 1 Down drive Speed profile CDC-U1 Speed Profile ! i J I " ! -j J i i LD«Aufaifi..aSjii. i i 23 CDC-U1 Accelerat ion profile 100 200 300 400 500 600 700 800 900 1000 Time (Sec) Figure 3.8 Route l Up drive Acceleration profile •> Simulation Paranwters- ADVISOR 2004 File Edit Units Condor Options Help info AV1L a d v i s o r 2 0 0 4 CDC 1U 400 500 600 time (sec) Speedj&evalion vs. Time O Description CDC 1U J 50 100 Sp&ed (km/h) time distance max speed: avg speed: max accel. max decel: avg accel: avg decel: idle time: no. of stops: max up grade: avg up grade: max dn grade: avg dn grade: 903 s 5 .92 km 47 3 km/h 23.57 kmfl-i 1.67 m/sA2 -1 75 m/s'2 0 29 mfeA2 •0.32 m/sA2 64 3 23 ! Drive Cycle ] CDC J U i Trip Builder j U ot cycles ] Cycle Filter initial Conditions F ] Constant Road Grade C...1 Interactive Simulation O Procedure [ 3 Acceleration Test Q C-radeabiiy Test j Accel Options [ Grade Options ""J Parametric Study i Q [ Flee Aux Loads j Save Help | Back J RUN Figure 3.18 Route 1 Up drive Speed profile 24 Route 1 Down Figure 3.10 is to show the speed Vs time for the CDC 1U drive cycle. The maximum speed is 21.91 kmph. Here we can see the speed is always varying in big margins through out the time scale. Figure 3.11 is to show the acceleration profde for the CDC 1U drive cycle. The maximum acceleration is 1.75m/s"2. The maximum deceleration is -2.03 m/s"". Fig 3.12 is the summery taken from the ADVISOR software after feeding all the relevant data collected during developing Colombo drive cycle. In this screen it provides all the relevant and valuable statistics of this drive cycle. 12 CDC-1D Speed Profile 10 Simulation Parameters ADVISOR 2004 Fiie Edit Units Condor Options Help info AW IL a d v i s o r 2 Q 0 4 I CDC 1D 100 200 300 400 500 600 700 800 t ime (sec) SpeeiiElevalion vs. Time . i G Description Q statistics CDC 1D ' * 50 100 Speed (km/h) 150 time: distance: max speed: avg speed- max accel: max decel avg accel: avg decei idle time: no ot stops max up grade, avg up grade max dn grade: avg dn grade 845 s 515 km 39 5 km/h 21.91 km/h 1.75 m/s*2 -2.03 m/sA2 0.27 m/sA2 -0.32 m/sA2 10 s 4 0 % 0 % 0 % 1 © I Drive Cycle ! Trip Builder Time Step j....) SOC Correction Q Constant Road Grade • interactive Simulation f oi cycles Cycle Filter J Initial Conditions n Acceleration Test ( } Gradeability Test f Accel Option; d J Parametric Study i j / [ _ Eilec Au>. L.oads | Mm-:: i Load Sim Setup Optimize cs vars j J L ,*} S ^ & O f t ParameiRT,. Figure 3.12 Route 1 Up drive Speed profile 26 Route 2 UP Figure 3.13 is to show the speed Vs time for the CDC 1U drive cycle. The maximum speed is 29.85 kmph. Here we can see the speed is always varying in big margins through out the time scale. Figure 3.14 is to show the acceleration profile for the CDC 1U drive cycle. The 2 J maximum acceleration is 1.75m/s . The maximum deceleration is -2.04 m/s" . Fig 3.15 is the summery taken from the ADVISOR software after feeding all the relevant data collected during developing Colombo drive cycle. In this screen it provides all the relevant and valuable statistics of this drive cycle. CDC-2U Speed Profile - O a> a> a. c0 0 100 200 300 400 500 600 700 800 Time(Sec) Figure 3.10 Route 1 Down drive Speed profile 27 CDC2U Acc n Profile CM c/> o o < »> S i m u l a t i o n P M m v A c i i--ASSVISOR 2 0 0 4 File Edit Units Condor Options Help info AVL 100 200 300 400 500 600 700 Time (Sec) Figure 3.14 Route 2 Down drive Acceleration profile a d v i s o r 2004f 800 key on •• speed elevation H /i * <• /V / !\ n I /' V 0 100 200 300 400 600 600 700 800 t ime (sec) SpeedXIIevation vs. Time o Description <*} Statis-tics J Drive Cycle j ; CDC_2U Time Step Q SOC Correction Q Constant Road Grade [ ' j Interactive Simulation * of cycles . J Cycle Filter initial Conditions Test Procedure Figure 3.15 Route 1 Up drive Speed profile Route 2 Down Figure 3.6 is to show the speed Vs time for the CDC 1U drive cycle. The maximum speed is 27.73 kmph. Here we can see the speed is always varying in big margins through out the time scale. Figure 3.17 is to show the acceleration profile for the CDC 1U drive cycle. The maximum acceleration is 1.67m/s"2. The maximum deceleration is -2.36 m/s"2. Fig 3.18 is the summery taken from the ADVISOR software after feeding all the relevant data collected during developing Colombo drive cycle. In this screen it provides all the relevant and valuable statistics of this drive cycle. CDC-2D Speed Profile t o ...j ^ uAjHiutW K1», 0 100 200 300 400 500 600 700 800 Time (Sec) 900 Figure 3.16 Route 2 Down drive Speed profile 29 CDC 2D Acc n Profile 2.5 2 1.5 1 0.5 0 o o < -0.5 CM 1/1 I if pll - 1 -1.5 - 2 -2.5 'M | I * \ : 1 , 1 WlF J i ! ^ j K M n r \l 1 0 100 200 300 400 500 600 700 800 900 Time (Sec) Figure 3.17 Route 2 Down drive Acceleration profile a d v i s o r 2 0 0 4 [ •> Simulation Parameters ADVISOR U W Fiie Edit Units Condor Options Help info MV\L CDC 2D 100 200 300 400 500 600 700 800 t ime (sec) Speed/Elevation vs. Time O Description 50 100 ' Speed (km/h) time: 841 s distance 6 49 km max speed: 59 km/h avg speed: 27 73 krn/h max accel: 1.67 m/sA2 max decel: -2.36 rruiV'2 avg accel: 0 .35 rn/sA2 avg decel: -0 41 m.'s"2 idle time: 72 S no of stops 25 max up grade 0 % avg up grade 0 % max dn grade. 0 % avg dn grade 0 % [ Drive Cycle j j CDC.2D Time Step O Correction j Constant Road Grade " ] Interactive Simulation f of cycles i • Cycle Filter Initial Conditions Test Procedure j j Acceleration Test Q Gradeabiliiy Test ] Accel Options O Parametric Study • [ Etec.Aux Loads j Load Sim. Setup j j Optimize cs Figure 3.18 Route 1 Up drive Speed profile 30 Route Distances Following four graphs were developed to evaluate the total lengths of four drive cycles respectively. In those figures length in meters were plotted against time in seconds. It shows how the length is increasing cumulatively with the time. Fig 3.19,fig 3.20,fig3.21 and fig 3.22 are respectively for Colombo drive cycle lup, ldown, 2up and 2 down. CDC-1U Total Distance 5000 4500 4000 3500 3000 £ 2500 a> c | ' — viAA: •fTTT} > L : i \ motor/ H w r L _s_T ontroller par electric acc power loads I < " , s < p b > Figure 4.3: Block diagram for parallel HEV in ADVISOR energy storage man TX.5SPO ' J 1 114 JQ Torque Coupling J0 J 0 • j T C . P U M H Y I Wieel/Axle j i Crr Crr j j WH_SMCAR I Accessory j Const j j | ? j Const - ! : ACC HYBRID _ B J pat JfTi man • j PTC_PAR : | Powertrain Control ( ] ) front wheel drive Q rear wheel drive ; • tour wheel drive j ? j Cargo Mass 135 V iew Block Diagram BD PAR Calculated. Mass 1661 override mass Variable List: Component torque_couplmg Variables tc loss | Edit Var . j : j Figure 4.4: Parameter setting for parallel HEV in ADVISOR 40 4.5.2 Toyota Prius -Japan car on ADVISOR Following is the simulink model used for Parallel HEV. All the set parameters shown in the figure are O c Clock Goto A drive cycle vehicle |Version & Copyright wheel and front/rear pnus p k , Fa nteractive Graphics H > / f c spd| L ^ 1~| NOx. PM (g/s) \ j exhaust sys Goto o motor/ ntroller C7V prius power] bus pnus gen/ controller Figure 4.5: Block diagram for Toyota Prius in ADVISOR •> Vehicle Input-ADVISOR 2804 File Edit Units Help info MWL a d v i s o r 2004 C o m p o n e n t fue l_conver ter Plot Select ion fc_ef f ic iency r ati Cosve?t«j GpaMtio® t.U. (*3kW) taint FA moi&i « * A N l <»ia 500 1000 1500 2000 2500 3000 3500 4000 Speed (rprri) j Load File j PWUS_JPN_default5_in J Drivetrain Config j p r i u s j p n (version) (""type Vehicle ! • I ! ? ) Fuel Converter Exhaust AftertreaS Energy Storage I rint Transmission VVheei/Axle j ; VEH_PRIUS_JPN j l j j Si - | i FC.PRIUS„ JPN ' IX si J j I ? ! rmnh | : ESS.NIMH6 j l j j J j J i I; MC_PRIUS JPN J 0 j j r«0 ifOj I r e 9 * [: GC.PRIUS.JPN j pgcvt v | j ? 11 pgcvt • f; TX_PRIUS_CVT_JPN Accessory JO J 0 F _ J i c , r ^ j I 7 j Crr - | WH_PR«JS_JPN I C o » * " U ? I Const - j ACC_PRIUS_JPN Poweitram Control • pnu... » J j ? pg PTC.PRIUS.JPN (;> front wheel drive rear wheel drive . ; four wheel drive j j ^ j V iew Block Diagram j Variable l ist: Component i fuel.converter Variables I fc_acc mass BD PRIUS JPN | Auto-Size Scale C o m p o n e n t s max pvvr peak eff mas-Ckvv) (kg) I 43 0 3 9 : 137 i #of rood V now ! ' 40 308 40 15 0.84 33 J Cargo Mass ^ 35 Calculated. Mass 1332 override mass 1368 J . } i X V Figure 4.6: Parameter setting for Toyota Prius in ADVISOR 41 4.5.3Conventional vehicle on ADVISOR Following is the simulink model used for Parallel HEV. All the set parameters are shown in the figure Figure 4.7: Block diagram for conventional vehicle in ADVISOR File Edit Units Help info AVL Vehicle Input a d v i s o r 2 0 0 4 Component Plot Selection fuel..conwerter fc .ef f ic iency rii*J Converter Operation J 300 250 - 2 0 0 : 160 100 50 i « FA w JJ.25 " f l ^ l ;00 1000 1500 2000 2500 3000 3500 4000 Speed (rpm) Load File i; MSC_cc | Dtivetram Config j conventional (version; ['"type"" VEH_PRHJS_JPN j _Auto-Size Scale Components maxpwr peak eff mass CKVV) (kg) 918 f Fuel Converter p i c ' ] j ? j s i " fl FC_PRIUS_JPN 108 : 03S 306 | Exhaust Allertretf | ; j | ? | - i. EX SI .."j Sol mod Vnom 2 8 J . i s J J 0 J .. J ; J 0 J ; ' J •('I "fr. 1 J j Transmission : man - i ^ ? j man jjl TX_5SMj j 1 114 J 0 ... "7 J i JM I j | Wheel/Axle j Crr j j | j J Crr WH_SMCAFf | Accessory | Const » f | ? | Const - ; ACC_ArtnexVI_i erHyb J Powertrain Control | conv - \ j ? man (*} front wheel drive • tear wheel drive ) four wheel drive Cargo Mass j View Block Diagram j B D _ C O N V Calculated. Mass • override mass Variable List: Component j fuel_convet1et Variables I fc_acc_mass jEdit Var. j 34 1 709 ^ A « < ' - t k , Figure 4.8: Parameter setting for conventional vehicle in ADVISOR 42 4.6 Specifications of the Selected Vehicle. The core vehicle used for this study has been a 4 - 1 production family sedan a decision made since similar size vehicles are more popular and which has been used throughout the study Specification of the components of the selected vehicle which are in specific Parallel Hybrid Electric Vehicle configuration are shown in following table 4.6, Table 4.2 Vehicle model specifications Parameter Value Total weight 1642 kg Chassis weight 1000 kg Frontal area 1.92 m2 Coefficient of Drag 0.32 Vehicle length 5.00 m Wheel Radius 0.29 m Engine Displacement 1.5 Engine Scale Factor 1 Transmission Manual, 5 speed Transmission efficiency 95% (constant throughout all gears) Gear ratios 3.5:2.14:1.39:1:0.78 Final drive ratio 3.98 Gear changes 1 - 2 and 2 - 1 @ 24 km/h 2- 3 and 3 -2 @ 40 km/h 3- 4 and 4 -3 @ 64 km/h 43 4- 5 and 5 -4 @ 75 km/h Motor/Generator Permanent Magnet Motor, 20kW continuous, 40kW peak Battery Advanced Battery, 40kW, 4kWh Battery Efficiency 85% The characteristics of the engine of the selected vehicle are represented by the following fuel consumption map and engine torque map which is derived based on the empirical data. Torque (Nm) Eng Speed (rpm) Figure 4.9 Fuel consumption map of the ICE of tested HEV Figure 4.9 and 4.10 shows the Engine efficiency map and efficiency contours of the tested vehicle. Motor efficiency of the selected vehicle as a function of speed and torque is represented by the Figure 4.10. Motor model in the vehicle simulation uses these 44 empirical data for calculating motor input power and hence to calculate the power extract from the battery. In this study. Battery efficiency during both charging and discharging is considered as constant and is taken as 85%. n oc. 300 350 Speed (rad/s) Figure 4.10 Engine fuel efficiency contours 45 CHAPTER 05 5.1 Resul t s and Analys i s 5.1.1 Sources to be analyzed For the efficiency analysis, following sources were identified and data has gathered by simulations. • Parallel HEV with maximum Efficiency can be achieved • Optimized PHEV with future predictions • Series HEV • Conventional vehicle with CVT • Conventional vehicle All those sources were simulated to collect data about fuel economy. The selected drive cycle was Colombo drive cycle which was explained in chapter 3 5.1.2 Results Table 5.1 QSS_TB Results Km/L CDC Route 1 CDC Route 2 Up Down Up Down Conventional wit CVT 39.20 41.77 37.71 47.62 Series HEV 19.52 56.18 21.80 20.53 Km/L CDC NEDC Conventional 11.62 13.04 Maximum Optimization 17.12 18.55 Optimized HEV with Future prediction(8sec) 15.71 14.83 46 5.1.3 Analysis Analysis of Drive Cycles with vehicle models Then this data can be analyzed with existing most common model drive cycles NEDC Table 5.2 Economy analysis Series HEV km/1 Conventional with CVT Km/1 CDC 1U 19.52 39.20 CDC ID 56.18 41.77 CDC 2U 21.80 37.71 CDC 2D 20.53 47.62 Europe NEDC 38.24 35.21 USA FTP Highway 37.86 41.79 Japan 10-15 mode 80 34.33 When considering the Conventional vehicles with CVT in standard drive cycles we can see that fuel efficiency is low in model drive cycles. (NEDC and Japan 10-15). We observed that conventional vehicle with continuous variable transmission is much more efficient than series HEV. The most probable reason is within the existing components the numbers of energy conversions are high in Series HEV. During these conversions there is always an energy loss. Cumulatively this leads to reduce the efficiency. If we compare the average over these drive cycle's the data will be as below. Series HEV 39.161 km/1 Conventional with CVT 39.661 km/1 47 90 80 70 60 50 40 30 20 10 0 n O n 9 r P S r j y q y ^ 6 • 9 ° r 9 r 9 r 9 o v o v a T . .0.3 100 200 300 400 GOO 600 700 8CO u_[radfe] 250 V- 200 Or f o / 150 •J / IV 100 m 9 » C O 50 0.8 fftfm 0.8 1 1 Battery charge ratio CD cr 400 200 > 500 Time [s] Battery current 1000 160 140 120 100 Battery \/oltage 500 Time [s] Battery power 1000 - 200 40 20 0 I— CD a -20 -40 500 Time [s] 1000 500 Time [s] 1000 Figure 5.2: Results of Series HEV in CDC 1U I 49 CDC ID Drive Cycle Results of Series Hybrid vehicle in CDC ID Electric Generator map •4 , f? \ » \ L. \ \ \ •-V \ o x \ <& I ' i SS S "Oil - 300 400 q |rad/s] 0.83 0 . 2 8 0.26 j 0.24 i 0.22' 400 200 < 03 Battery charge ratio > 500 Time [s] Battery current 1000 -200 160 140 120 100 40 Battery voltage ' j. I j n | , J^ OTWffT 500 Time [s] Battery power 1000 20 CD CL 0 500 Time [s] 1000 -20 W W Vi 500 Time [s] 1000 Figure 5.8: Results of Series HEV in Japan 10-15 50 CDC 2U Drive Cycle Results of Series Hybrid vehicle in CDC 2U Combustion Engine map oK 200 300 400 500 600 700 800 a — [rad/s] Electric Generator map Electric Motor map 200 r o ' 6 p o o i t / f r % 1 2 , . Vs .2 -f| 45 1 2. 1 2 . 0.8 0.86... . . : . . :^, v. 300 400 [rad/s] A \ L S X v \ ^ \ \ r> 6 \ CP, \ „ ^p \ \ -Q 7f> 0.8 H 200 300 400 "'eg lrad/s) 0.3 0 . 2 8 0.26 i 0.24 Battery charge ratio 0.22 500 Battery voltage 0 200 400 600 800 Time [s] Battery current 40 20 0 -20 -40 0 200 400 600 800 Time [s] Battery power if | I I PL j iUi H I i r f m i , | / i fV| 0 200 400 600 800 0 200 400 600 800 Time [s] Time [s] Figure 5.4: Results of Series HEV in CDC 2U 51 CDC 2D Drive Cycle Results of Series Hybrid vehicle in CDC 2D J K\ . Combustion Engine map 100 200 300 400" 500 " 600 700 » l c E [rad/s] Electric Generator map o.s i x / \ \ i I w \ s \ - W 4> 100 200 300 400 500 Electr ic Motor map -lOObVif 'too JSP® -150 h -200 f •250 r 0 > Q O \ No \ ° o\ o » - :< 5? w' 100 200 300 400 M a , [rad/s] 500 600 Battery charge ratio 4001 200! 500 Time [s] Battery current Wf 0 mi ! | I in fit 1000 -200; -400 Battery voltage Q_ 0 500 Time [s] 1000 500 Time [s] Battery power 1000 500 Time [s] 1000 Figure 5.5: Results of Series HEV in CDC 2D 52 European NEDC Results of Series Hybrid vehicle in NEDC Combustion Engine map u 601 \ _ ,J±! 0 ^ 2 / / /-...„• - J 3 iK j 0 2 7 5 +0.2 0.1 a _ 1 oC E{rad/s] Electric Motor map f^jmz . "ifts Z P ^ ^ i f e ] fM&feocfeoo ] V-COGOOOOC i Eleclric Generator map o A » \ ~ "O-O.83 0.® 0 ™ 200 300 <00 500 600 » „ [rad/s] 9|«5— -Vv X . !f 100 200 300 400 500 600 oia-[rad/s] 0.26 0.24 t- CQ a" 0 . 2 2 Battery charge ratio w A, 0.2 500 1000 Time [s] 160 Battery voltage 500 1000 1500 Time [s] Battery current 100 500 1000 Time [s] Battery power 1500 500 1000 Time [s] 1500 1500 Figure 5.8: Results of Series HEV in Japan 10-15 53 US FTP High way Results of Series Hybrid vehicle in US FTP highway N/ Combustion Engine map 100 200 300 400 500 600 700 800 W c E [rad/s] Electric Generator map » s / V \ \ i n \ • • ttSi 300 400 [rafl/s] E lect r i c Motor map 0.25 0.24 Battery charge ratio cT 0.23 1 / 0 . 2 2 ! 200 Battery voltage 0 200 400 600 800 Time [s] Battery current 0 200 400 600 800 Time [s] Battery power 400 2 0 0 i. 50 Oil -200 I |fy -400 0 0 200 400 600 800 Time [s] -50 (Ml,.J i" ^ I'liNyv"y I m A • i I; 0 200 400 600 800 Time [s] Figure 5.7: Results of Series HEV in FTP Highway 54 Japan 10-15 Results of Series Hybrid vehicle in Japan 10-15 Combustion Engine map 100 200 300 J-Jil- <00 500 600 700 .oCE [rad/s] Electric Generator rrap P 3l V I V X v \ X / \ b ! ° ; v - > I % \ \ \ K V '00 200 083 j 08 Mg. [rad/s] MO 600 250 200; 150 L Electric Molor map "T .9 . 50.I- y , a a x K s i a i v , S" | " * * . -100 -150 -200 -250 •SOU ccafifo 0.85 -mm, ' ,* . . . . \ aato^ gy—q j n — \ r fi 0.25 0.24 100 200 300 400 . J3 33.' ,5) K ?v< S^-i O - y 0 275 L I t t i". / s m i o S'MSS 1 p ° l f m J2J 100 200 300 400 500 600 700 800 coCE [rad/s] Figure 5.14: Results of Conventional vehicle in FTP Highway 57 Japan 10 15 Combust ion Engine map Figure 5.15: Results of Conventional vehicle in Japan 10-15 CHAPTER 06 ADVISOR Results 6.1Simulation Results in ADVISOR In summery following table describes the results. All the relevant result sheets are attached to the end of this chapter Table 6.1 ADVISOR results kM/L CDC 1 U CDC 1 D CDC 2 U CDC 2 D NEDC J 10 - 15 US 06 Parallel HEV 16.6 17.24 18.18 16.6 18.51 16.129 18.18 Toyota Prius 25.6 23.8 24.39 23.25 19.6 16.12 16.6 Conventional 5.12 5.12 6.21 5.68 6.711 4.97 9.34 Distance (kM) 5.9 5.1 6.5 6.5 10.9 4.2 12.9 Time(Sec) 930 845 784 841 1184 660 600 Max. Speed(km/h) 47.8 39.5 57.9 59 120 69.97 129.23 Avg. Speed (km/h) 23.57 21.91 29.85 27.73 33.21 22.68 77.2 Avg. AccAn (msA-2) 0.29 0.27 0.33 0.35 0.54 0.57 0.67 Avg. decAn (msA-2) -0.32 -0.32 -0.41 -0.41 -0.79 -0.65 -0.73 Idle time(Sec) 64 10 37 72 298 215 45 Compar ison of fuel e c o n o m y - » — P a r a l l e l H E V « • — T o y o t a P r i u s C o n v e n t i o n a l U D U D 1 5 D r i v e c y c l e s 59 TOYOTA Prius in CDC Drive cycle C = i Toyota Prius — A v e r a g e Fig 6.1 Graph of fuel economy When studying the Table 6.1 and the chart 6.1 it is obvious that the optimizing process is essential and very effective to improve fuel economy. The best example is Toyota Prius car. The optimized duel mode HEV is showing a real edge over other models. Even in Toyota Prius the most significant is it is more economical in complex or irregularly varying drive cycles such as CDC. This difference is clearly visible in the chart in figure 6.1. According to the simulation results the conventional vehicle shows maximum fuel economy only when the speed variation is very minimum. That means the vehicle is cruising without any acceleration or deceleration. The simulation results for US 06 highway drive cycle is an example. The dfault parallel HEV model results are indicated between the two extremes explained above. Always it is better than the conventional vehicle and not so close to optimized Toyota Prius. In Colombo drive cycles the Toyota Prius shows better fuel economy than in other selected standard drive cycles. That proves it is essential to cater those unpredictable and always changing factors in drive cycles. It is very important to simulate all vehicles in such drive cycles as CDC for performance evaluating. Also in HEV optimization it is essential to consider these situations in their optimization processes to obtain better performances. 60 6.2 Parallel HEV in ADVISOR 6.2.1 Results for CDC 1U Drive cycle •> Results ADVISOR 2004 File Edit Tools Units Help info a d v i s o r 2004 | t t t t _ i h e c o / 1 0 - - — f c - . . - . — 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 800 9 0 0 1 0 0 0 I : | Ill J Li i c _ t r q _ o u t _ a i i . i . . . i . . . . . . i i i i i 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 R e s u l t s f igure plc< control C o m p o n e n t motor ..controller j j Plot Variable (Select Ax is First) mc_trq_out_a | ( T ) * of plots 4 J Fuel Consumption (LAI 00 km) Gasol ine Equivalent Dis tance (km) E m i s s i o n s ( g r a m s . ' k m ) | s ta HC CO NOX PM 1.514 1.474 0.173 0 A c c e l e r a t i o n T e s t 0-96.6 km/h (s): nA 64.4-96.6 km/h (s): n / 0-136.8 km/h (s) : n * Max. Acce l . (m/sA2): n /a Distance in 5 s (m): n /a Time in 0.4km (s); n /a Max. Speed (kmph): n /a n/a % Energy Use Figure Output Check Plots Compare Results W i t h | S imPata | | Test Data Warn ings /Messages Replay j | Back T w o Help j Back j Exit 500 . 400 300 • 200 100 0 ,70 .75 Figure 6.2: Results of parallel HEV in CDC 1U Motor/Control ler Operat ion W e s t i n g h o u s e 7 5 - k W (cont inuous) AC induct ion mo to r / i nve r t e r —x— max cont. motoring torque • — max motoring torque -0-— max cont. gen. torque ~©— max gen. torque actual operating points © (. * — © 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Speed (rpm) Figure 6.13: Motor efficiency of parallel HEV in Japan 10-15 61 6.2.2 Results for CDC ID Drive cycle •> Results—ADVISOR 2004 File Edit Tools Units Help info J I V L _ adv isor 2004 4 0 3 a | 20 • If V \ ''MJ. v l fVL J 1/11 " V f - c y c _ k p h _ r - kpha if r y i L 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 | 0 05 E o> 0 15 I 10 I 5 O 0 i i i i i i i h e • v i l l i c o / 1 0 • 4 n o x J A - p m i i i i 1 h h * - ,„,„ 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 a x r o w u i y V J tt—r _J I 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 S J i t d l Resul ts f igure plot control C o m p o n e n t : fuel! conver ter Plot Variable (Select Axis First) f c . b r a k e j r q J T ) * 0< p,0ti 4 J Fuel Consumption (L/100 km) Gasoline Equivalent Distance (km) 5.8 E m i s s i o n s ( g r a m s r i t m ) HC CO NOx 1.77 1.617 0.156 A c c e l e r a t i o n T e s t 0-96.6 kitvh ($): n /a 64.4-96.6 krrWh (sX n/a 0-136.8 k rwh(s ) ; n /a Gradeab i l i t y : Max. Accel . (m/s*2): n /a Distance In Ss (m): n /a Time in 0.4km (sX n/a Max. Speed (kmph): n /a n/a % Energy Use Figure . . . i t Output Check Plots Compare Results With: j SimData Warnings/Messages S B O H H B I B i H M/txr-rtrnKoftw... :•> ReaJB-ADvisOft20... Figure 6.4: Results of parallel HEV in CDC ID • u 5 0 0 , 400 - 300 200 100 2 g" 0 1 -100 -200 -300 -400 -500 Motor/Control ler Operat ion Wes t i nghouse 7 5 - k W (cont inuous) AC induct ion mo to r / i nve r te r —*— max cont. motoring torque * max motoring torque O max cont. gen. torque - O — max gen. torque actual operating points n r r 0 . 8 0.7 0.75 0.85 & e & 0 8 B ffi- 1000 2000 3000 4000 5000 6000 7000 8000 9000 Speed (rpm) Figure 6.13: Motor efficiency of parallel HEV in Japan 10-15 10000 62 6.2.3 Results for CDC 2U Drive cycle B W W M File Edit Tools Units Help info A V L he - c o / 1 0 - n o x H J M J t A l f t ^ « l » ii.,,L , . , > „ , . • . 4 - 1. . 1 . . . 1 p m - Compare Results With; | s imData | [ Test Data Warnings/Messages S S E I Results figure plot control C o m p o n e n t • fuel_convertar j J Plot Variable (Select Axis First) f c .b r ake. t rq - £ j * of plots 4 Fuel Consumption (L f l 00 km) Gasoline Equivalent Distance QtnO 5.5 5.5 6.5 E m i s s i o n s ( g r a m s ' k m ) | stai HC CO NOx PM 1.244 1.455 0.243 0 A c c e l e r a t i o n T e s t 0-96.6 km*« (s); n/a 64.4-96.6km* i (s ) : n/a 0-136.8 km*i (s): n fe Max. Accel . (m/sA2): n /a Distance in 5s (m): n/a Time m 0 4km (s): n/a Max Speed (kmph)- n /a n/a % Energy Use Figure Output Check Plots Figure 6.6: Results of parallel HEV in CDC 2U 500* 400 - 300 T 200 Motor/Control ler Operat ion Wes t inghouse 75 -kW (cont inuous) AC induct ion moto r / i nve r te r .0.8 0.85 . o.a. a) 0 D F o H -100 - 2 0 0 -300 -400 -500" 0.70.75 1 00 I 0.9 0.85 ^ 4 - _ 'x Q.9 - 0 . 8 . . A . ..0.85 - O r —O o e - - f c r -*— max cont. motoring torque * max motoring torque - 9 — max cont. gen. torque - O — max gen. torque x actual operating points 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Speed (rpm) Figure 6.13: Motor efficiency of parallel HEV in Japan 10-15 63 6.2.4 Results for CDC 2D Drive cycle «> .Resul ts - -ADVISOR 2 0 0 4 File Edit Tools Units Help Info advisor 2004 60 r r\i 20 A , » 1 V A f \ 1 i J _J i — ,JL, a a 0.7 r •£ 0.6B • a in 0 64 • 100 200 300 400 600 600 700 800 900 100 2 0 0 300 400 600 600 700 800 900 0.2 0.16 0.1 0.05 100 200 300 400 500 600 700 800 900 H J • overall ratio U 100 2 0 0 300 400 600 600 700 800 900 - > H«aias-ACI* lSpRJp.. Results figure plot control Componen t fuel_converter Plot Variable (Select Axis First) te .brake J r q J T j # of plots 4 J Fuel Consumption (L/100 km) Gasokne Equivalent Distarice (km) Emiss ions (g rams/km) HC CO NOx 1.337 1.387 0.216 Accelerat ion Test 0-96.6 km/h (s). n/a 64.4-96.6 km/h (5)' n/a 0-136.8 km/h (s): n/a Max. Accel. (m/sA2): n/a Distarice in 5s (m): n/a Time iri 0.4km (s); n/a Max. Speed (kmph); n/a n/a % Energy Use Figure Output Check Plots Compare Results With: J sim Data WamirigsMessages Figure 6.8: Results of parallel HEV in CDC 2D 500 f 400 r 300 * 200 100 L U x Motor/Controller Operat ion West inghouse 75-kW (continuous) AC induction motor / inver ter 0.8 0.85 0.9 0.8 __ 0.85 0) 3 F o 0 . i m u m m . ibc . " • • 1 " " - > ' 0.85 0.7 0.8 0.75 - 1 0 0 0.7 -200 ' 0.75 -300- -400 -500 •' - e - - & -o- -©- G © - x — max cont. motoring torque *- max motoring torque •••€> max cont. gen. torque <~> max gen. torque * actual operating points - e - 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Speed (rpm) Figure 6.13: Motor efficiency of parallel HEV in Japan 10-15 64 6.2.5 Results for NEDC Drive cycle File Edit Tools Units Help info A V L w m s m m m m m a S B S M m advisor 2004 c y c _ k p h _ r - k p h a _ r ~ \ r ^ \ — 1 • A A / \ n \,r\ 1 \ / \ A A A „ A A / \ n / 11 \ . l 200 4 0 0 1200 0 . 6 6 0 . 6 4 i 0.2 0 . 1 5 0 . 1 0 . 0 5 | e s s _ s o c _ h i s t | " V ^ ^ he c o / 1 0 p m - L ,', , . . Results f igure C o m p o n e n t fuel .converter Plot Variable (Select Axis First) f c .b rake . t rq J T ) * ° f plots 4 J Fuel Consumption (1/1 00 km) Gasoline Equivalent Distance (km) E m i s s i o n s ( g r a m s ' k m ) HC CO NOx 0.9 0.832 0.121 0 | Standards PM A c c e l e r a t i o n T e s t 0-96.6 km* i (s); n/a 64.4-96.6 km^i (s): n/a 0-136.8 krn/h(s): n/a Gradeab i l i t y : Max. Accel . (m/$*2): n /a Distance in 5s (m): n/a Time in 0.4km (s): n/a Max Speed (kmph): n/« n/a % Energy Use Figure Output Check Plots Figure 6.10: Results of parallel HEV in NEDC 500 f 400 300 r 200 100 0 - 1 0 0 -200 -300" -400 -500' Motor/Controller Operation Westinghouse 75-kW (continuous) AC induction motor/inverter •0.7 0.75 0.8 0.85 0.9 0 , — -U.85 + + . O 9 .X. JC .JC .X 0.35 .°-8 - o - - e - • © C £) - © - k zsiz max cont. motoring torque max motoring torque max cont. gen. torque max gen. torque actual operating points 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Speed (rpm) Figure 6.13: Motor efficiency of parallel HEV in Japan 10-15 65 6.2.6 Results for JAPAN 10-15 Drive cycle File Edit Tools Units Help info AVL GO | 4 0 20 0 0 7 2 t 0 . 7 I' 0 68 u 0 66 , advisor 2004 Results f igure - c y c _ k p i l _ r • kptia v \ M / \ / ,i I MA / v \ n A n P A —!—I 1—L 4 0 0 600 600 7 0 0 1 0 0 2 0 0 3 0 0 4 0 0 600 600 C o m p o n e n t luel_conveiter Plot Variable (Select Axis First) t c j v a t e . t r q J T ] * of plots 4 T ] Fuel Consumption (LflOO km) Gasoline Equivalent Distance (km) 6.2 6.2 E m i s s i o n s ( u r a m s ^ k m ) HC CO NOx 2.043 1.876 0.225 A c c e l e r a t i o n Tes t 0-96.6 kmAi (s): n/a 64.4-96.6 kmih(s>: n /a 0-136.8 km*l (3); n a Max Accel . (m/s*2): n/a Distance in 5s (mr n/a Time in 0 4km (s): n/a May Speed (kmph) a 'a n/a % E z max gen. torque * actual operating points 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Speed (rpm) Figure 6.13: Motor efficiency of parallel HEV in Japan 10-15 66 6.2.2 Results for US 06 Drive cycle File Edit Tools Units Help info AVL advisor 2004 1 5 0 p 100 - 60 -V V i I \ I - c y c _ k p h _ r • kph3 0 0 . 7 u> 0 . 6 8 o o 0 6 6 &> 0 . 6 4 0 . 6 2 0 4 0 . 3 C 5> in 0 2 F 4J 0 , 1 0 1 5 U _ 100 3 0 0 [ M L 200 600 he ' j co/10 - | U - f i . • Results f igure C o m p o n e n t iuel_converter J L i plot control Pied Variable (Select Axis First) tc_bre \ I \ \ • cyc_kph_r kpha n K 100 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 700 8 0 0 9 0 0 1000 • e»s soc hisl „ 0 1 5 i 1 0.1 E * ' 0 . 05 he co/10 " nox • p m 1 o 0 5 E % 0 100 2 0 0 3 0 0 4 0 0 SOD 6 0 0 700 BOO S 0 0 1000 •0.5 • •1 0 100 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 0 Mvtw - f f c w w f t W . . . 2 0 . . . Results figure C o m p o n e n t fuel.converter plot cont Plot Variable (Select Axis First) f c j j r a k e j r q f f?~| * o f p t o l s Fuel Consumption CL/100 km) Gasoline Equivalent Distance (kin) E m i s s i o n s ( g r a m s / k m ) [ Standard* HC CO NOx PM 1.368 1.323 0.123 0 A c c e l e r a t i o n Test 0-96 6 km/h (s): n /a 64 .4 96.6 km/h (s): n /a 0-136.8 km/h(s): n/a Max Accel. (m/sA2): n/a Distance in 5s (m) n /a Time n 0 4km (s): n/a Max Speed (kmph): n /a n/a % Energy Use Figure Output Check Plot s Compa/e Resu ls With S m Data j [ Test Data Wa» rungs/Messages Prius Speeas it. I Figure 6.16: Results of Toyota Prius in C D C 1U 6.3.2 Results for CDC ID Drive evele File Edit Tools Units Help info A V L a d v i s o r 2004 4 0 30 1 20 10 0 0.8 » 0 . 7 I 06 S 0 . 5 0 4 I 0.08 . »06 a D 5 0 04 E " 0 02 r \ \ / \ K t A > V \ N • • cyc_kplij kpha v t A 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 100 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 \ • \ \ i i , • he co/10 pm Jl.Ar\ ALT i . l i t r. ...,i..U ii JU.A I • 5 0 0 O 0.5 2? % 0 a> o -0.5 -1 . ' • S M I Resul ts f igure C o m p o n e n t fuel_converter J plot control Plot Variable (Select Axis First) fc_brake_trq J B # ot plots 4 Fuel Consumption (L/100 km) 4.2 Gasoline Equivalent 4.2 Distance (km) 5.1 E m i s s i o n s ( g r a m s / k m ) } Standards j HC CO NOx PM 1.40 1.393 0.093 0 A c c e l e r a t i o n T e s t 0-96.6 km/h (s): n/a Max. Accel. (m/sA2): n/a 64.4-96.6 kmfri (s): n /a Distance in 5s (m): n/a 0-136.6 km/h (s). n/a Time in 0.4km (s). n/a Max.. Speed (kmph): n/a Gradeab i l i t y : n/a % Energy Use Figure Output Check Plot s Compare Results With: [ Sim Data j j Test Data ] Warnings/Messages ir I Prius Speeds 'A* Figure 6.21: Results of Toyota Prius in Japan 10-15 68 6.3.3 Results for CDC 2U Drive cycle Pile Edit Tools Units Help info /JliVIL i 1 i a d v i s o r 2 D G 4 60 4 0 € E 20 0 0 .7 S 0 . 6 5 o ' 0 . 6 S 0 . 5 5 0.6 I 0 4 0 . 3 M r t i h rJ h i A1 A A / / 1 • cyc_kph_r kpha / — r p ; 7 r / n j A 1 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 • e s s _ s c c _ h i s i 100 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 Compare Results With: j sim Dala J [ ' Test Data" Warnings/Messages | p r i U s Speeds Results figure C o m p o n e n t fuel .converter Plot Variable (Select Axis First) fc_brake_trq Fuel Consumption (L/100 km) Gasoline Equivalent Distance (km) e m i s s i o n s ( g r a m s / k m ) S standards HC CO NOx PM 1.169 1.343 0.19/ 0 A c c e l e r a t i o n Tes t 0-96.6 km/h (s): n/a 64.4-96.6 km/h (s): n/a 0-136.8 km/h (s): n/a Energy Use Figure Max Accel, (m /s^ ) : n/a Distance in 5s (m): n/a Time in 0 .4km (s): n/a Max. Speed (kmph): n/a n/a % Output Check Plots Figure 6.18: Results of Toyota Prius in CDC 2U 6.3.4 Results for CDC 2D Drive cycle advisor 2004 -> Results-ADVISOR 2004 File Edit Tools units Help W o MVL B H i S Figure 6.19: Results of Toyota Prius in CDC 2D 69 6.3.5 Results for NEDC Drive cycle File Edit Tools Units Help info MV\L advisor 2D04 1 5 0 100 50 0 i 0 .7 0 .65 l 0 .6 ; 0 55 0 5 I 0.1 S 0 .05 £ c y c _ k p h _ r k p h a J \ / i -r\ ' \ r, i \ ' A / \l \.f\ 1 \ ! \(\l \ i \r I'M - e s s soc h is t he i co/10 nox A p m , 1 /C — « » — — f F f — a V 1 1 Irfa — U P I- Compere Results With: | Sim Data j [ Test Data J Results f igure C o m p o n e n t fuel .converter Plot Variable (Select Axis First) fc .brake.Jrq fue l Consumption (U100 km) Gasoline Equivalent Distance (km) E m i s s i o n s ( g r a m s / k m ) HC CO NOx 0.785 0.773 0.108 Acce le ra t i on Tes t 0-96.6 km/h (s); n/a 64.4-96.6 krn/h ($): n/a 0-136.8 km/h (a): n/a Max. Accel. (m/sA2). n/a Distance in 5s (m): n/a Time in 0.4km (s): n/a Max Speed (kmph): n/a n/a % Energy Use Figure Output Check Plots Figure 6.20: Results of Toyota Prius in N E D C 6.3.6 Results for JAPAN10-15 Drive cycle •> Results ADVISOR 2004 File Edit Tools Units Help info AVL advisor 2004 Results f igure cyc_kph_r kpha ' a f A n \ n / \j \ \ C o m p o n e n t Tuel converter 'J Plot Variable (Select Axis First) f c .b r a k e j r q • j p T f Fuel Consumption (1/100 krn) Figure 6.21: Results of Toyota Prius in Japan 10-15 70 6.3.7 Results for US 06 Drive cycle File Edit Tools Units Help info I l l l S t f M f l AVL 0 .7 0 . 6 0 s 0 3 0 2 0 1 advisor 7D04 • c y c _ k p h _ r k p h a i\h A A / 1 j J I m 1 • \ i \ he c o / 1 0 • n o x p m m i x Results f igure C o m p o n e n t (a): n/a Gradeabi f i ty r Max Accel (m/sA2X n/a Distance in 5s (m) n/a Time in 0 4km (s): n/a Max. Speed (kmph); n /a n/a % Energy Use Figure Compare Results With. VVai ningsAtes sages Output Check Plots n Data j Test Data • > Rrau l ts -MWISOK Jf l . . . Figure 6.23: Results of Conventional vehicle in C D C l U 300 250 200 150 1 0 0 50 0 -50 -100 -150 500 m ^ f — max torque curve operating points(gear 1) % « operating points(gear 2) O operating pomts(gear 3) p operating points(gear 4) operating points(gear 5) downshift 2->1 downshift 3->2 downshift 4->3 downshift 5->4 upshift 1->2 upshift 2->3 ~ • upshift 3->4 upshift 4->5 1000 1500 2000 2500 Speed (rpm) 3000 3500 4000 300 250 200 150 i z 100 a 50 »- 0 -50 -100 -150 ] max torque cune output shaft op. pts(includes inertia & accessories) 500 1000 1500 2000 2500 3000 3500 4000 Speed (rpm) Difference between requested and achieved speeds 100 200 300 400 500 600 700 800 900 1000 time (s) 0.4 0.35 0 3 0 25 0 . 2 0.15 0 . 1 0 05 Fuel Converter Efficiency X " " J- V ' t> • » J » j . * - * V * « • » v * X 55 * " x , . * " , < / * ; ' » , - 4 f t , X , 0 100 200 300 400 500 600 700 800 900 1000 time (s) Figure 6.24: Motor eff iciency of Conventional vehicle in C D C 1U 72 6.4.2 Results for CDC ID Drive cycle " " " File Edit Tools Units Help JIVIL • i iiiiiiiwni advisor 2D04 300 2 5 0 2 0 0 150 i 100 - 501 0 - 5 0 - - 1 0 0 40 30 | 2 0 1 0 0 2 « 1 5 i 1 0.5 0 I 0 .4 0 . 3 | 02 I * 0 .1 U \ / \ ••A. ft l \ A IV i r l, J y li / 1/ - c y c _ k p h _ i - k p h a Vu \ „ v r 5/ 100 200 300 400 500 600 700 300 900 [•""• f c j u e i _ r a t e | _ i i 100 2 0 0 3 0 0 4 0 0 5 0 0 600 700 3 0 0 900 (s): n/a Max Accel (,mJs"2) n/a Distance in 5s (rn): n/a Time in 0.4krn (s): n/a Max Speed (kmph) ri.'a n/a % L Energy Use Figure Output Check Plots Compare Results VMth: | SirnDala | j Test Data" VVamintjs/Messages E t l Figure 6.25: Results of Conventional vehicle in C D C I D Shift Diagram • Fuel Converter Prius pn 1.51 (43kW) from FA model and ANL test data 4 - 1 5 0 — 5 0 0 m a x t o r q u e c u f \ e o p e r a t i n g p o i n l s f g e a r 1) o p e r a t i n g p o i n t s ( g e a r 2) 0 o p e r a t i n g p o i n t s f g e a r 3) Cff o p e r a t i n g p o i n t s f g e a r 4) ope ra t i ng po in t s (gea r 5) downsh i f t 2 - > 1 downsh i f t 3 - > 2 downsh i f t 4 - > 3 downsh i f t 5 - > 4 upsh i f t 1 - > 2 upsh i f t 2 - > 3 upsh i f t 3 - > 4 upsh i f t 4 - > 5 1 0 0 0 1 5 0 0 2 0 0 0 2 5 0 0 S p e e d ( rpm) 3 0 0 0 3 5 0 0 4 0 0 0 Fuel Converter Operation Pnus pn 1 51 (43kW; I rpm FA model and ANL test data 300 2 5 0 r 2 0 0 [ 150; i 1 0 0 y I | r SO- O r - 5 0 i - 1 0 0 - 1 5 0 ; 0 S i 5 3 4 5 "m - • 3 ? : 5 ~ 30.5 22 5 . % X 4 m a x l o r q u e c u n e ou tpu t sha f t op, p t s ( i n c l u d e s iner t ia & a c c e s s o r i e s ) 5 0 0 1000 1500 2 0 0 0 2 5 0 0 3 0 0 0 3 5 0 0 4 0 0 0 S p e e d ( rpm) Dif ference b e t w e e n reques ted and ach ieved speeds 100 200 300 400 500 600 700 800 900 t i m e (s) 0 .35 & , • ,» » , J * * 0 2 5 - i ^ • > 7 * " S . . < « i " VYS'* .v- f ' . ' • « > r ? i » • " } * « Fuel Convener Ef f i c iency >. 0.2 £ i' o « 0 15 0.1 j 100 2 0 0 300 400 500 6 0 0 700 800 9 0 0 t i m e (s) Figure 6.24: Motor eff iciency of Conventional vehicle in C D C 1U 73 -100 200 300 400 500 600 700 • he co/10 • nox pm L aJ 100 200 \ r - 400 500 KM V A U K 100 200 300 400 500 600 700 800 Emiss ions (g rams/km) Standards HC CO NOx PM 3.711 4.487 0.807 0 Acceleration Test 0-96.6 km/h ($): n/a 64.4-96.6 krruh(s): n/a 0-136.8 kmjh (s): n/a Max. Accel. (m/sA2): n/» Distance in Ss (m): n/a Time in 0.4km (3£ n/a Max Speed (kmph): n/a n/a % Energy Use Figure Output Check Plots Compare Results VVtth: j Sim Data WarningsMessages S l ^ H H H i H i ( Test Data M taiS-AOVISORZQ... 300 250: 200 j- 150 j- 100 i 50 r Oh -601 -100 h l t i k i - • Figure 6.27: Results of Conventional vehicle in CDC 2U >1 Operation • 4 6 aa.- m ILL m a x to rque curve operating points(gear 1) operating points(gear 2) operating points(gear 3) operating points(gear 4) operating points(gear 5) downshift 2->1 downshift 3->2 downshift 4->3 downshift 5->4 upsh i f t 1 - > 2 upsh i f t 2 - > 3 upshift 3->4 upsh i f t 4 - > 5 - 1 5 0 ; — 500 1000 1500 2000 2500 Speed (rpm) 3000 3500 4000 300 250 200 150 | 100 a> I 50 t- 0 •50 -100 -150 - max torque cur\« output shaft op. pts(includes inertia & accessories) Difference between requested and achieved speeds 1000 1500 2000 2500 3000 3500 Speed (rpm) Fuel Converter Efficiency i | 0 2 i 1 »«»» <* ti •. v < J V " " « * . . • * •> * . • • * „ - t a «V . 5 . 'v it . « »a?5 « » 100 200 300 400 time (s) 500 600 700 300 400 500 time (s) 600 700 Figure 6.24: Motor efficiency of Conventional vehicle in CDC 1U 74 6.4.4 Results for CDC 2D Drive cycle a m a r — — ^ File Edit Tools Units Help javiL advisor 2004 S 2 l ' 5 1 0 I 0 . 4 0 .3 1 02 I 10 1 a f l / s A I I / ! / I I I i ' / V1 A ~ ! A - cyc_kph_r • kpha i 100 200 300 400 500 600 700 800 900 w 100 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 300 h, he CO/10 - nox - pm 100 200 300 400 500 600 700 800 900 JJ A - J l j overall ratio | \ \ J I V Rioulte-MJVJSOR » . . . R e s u l t s f igure plot dtmtrol C o m p o n e n t •uel_convertar Plot Variable (Select Axis First) Fuel Consumption 1 downshift 3->2 downshift 4->3 downshift 5->4 upshift 1->2 upshift 2->3 upshift 3->4 upshift 4->5 I00 1500 2000 2500 3000 3500 4000 Speed (rpm) Difference between requested and achieved speeds 100 200 300 400 500 600 700 800 900 time (s) 300 250 200 150 | 100 0) I 50 b- 0 -50 -100 -150 1 0 4 0.35 0.3 025 0 1 0.2 £ 0 0.15 0 . 1 0.05 0 5 • V , / ,22 5. ' . V ' f - max torque curve output shaft op. pts(includes inertia & accessones) 500 1000 1500 2000 2500 3000 3500 Speed (rpm) Fuel Converter Efficiency * * r Mi* J* ? " v h * ?? * / 100 200 300 400 500 600 700 800 time (s) Figure 6.24: Motor eff iciency of Conventional vehicle in C D C 1U 75 6.4.5 Results for NEDC Drive cycle Pile Edit Tools Units Help info ,AVL advisor 2004 150 100 50 0 3 5 2 - c y c Kph r k p h a — 7 r - — ' \ A , n A i / 1 he co/10 nox A pm n VJ u I u V •> Rmto -ADVISORJO. . . B B B R e s u l t s f igure Componen t fuel_converter Plot Variable (Select Axis First) - * p l o t 5 4 Fuel Consumption (L./I00 km) Gasoline Equivalent Distance (km) 14.9 14.9 10.9 E m i s s i o n s (grams. 'km) Standards HC CO NOx PM 2.532 2.765 0.473 0 Acce le ra t i on Test 0-96.6 km/h (s): n/a 64.4-96.6 km/h (s): n/a 0-136.8 km/h (s): n/a Max. Accel. (m/s"2): n/a Distance In Ss (m): n/a Time in 0.4km (s): n/a Max. Speed (kmph): n/a n/a % Energy Use Figure j j Output Check P Compare Results With: Sim Data j Test Data warnings/Messages j Back am Figure 6.31: Results of Conventional vehicle in N E D C 300 250 200 150 | 100 I H 0 -50 -100 ^ O 0 ^ o -150 — 500 max torque curve operating points(gear 1) C operating points(gear 2) 0 operating points(gear 3) operating points (gear 4) operating points(gear 5) downshift 2->1 — __ downshift 3->2 downshift 4->3 downshift 5->4 upshift 1->2 upshift 2->3 upshift 3->4 upshift 4->5 1000 1500 2000 2500 3000 3500 4000 Speed (rpm) Difference between requested and achieved speeds 300 250 200 150 | 100 1 downshift 3->2 downshift 4->3 downshift 5->4 upshift 1->2 upshift 2->3 upshift 3->4 upshift 4->5 500 1000 1500 2000 2500 3000 3500 4000 Speed (rpm) 300 250 200 150 1 100 ' 1 > * \ y * * i 0 .25 . 0.2 0 .15 • 0 . 1 - 0.051- : "r r • r r ? i il «< 300 400 time (s) 100 200 300 400 500 600 time (s) 700 Figure 6.34: Motor eff iciency of Conventional vehicle in Japan 10-15 77 6.4.7 Results for US 06 Drive cycle File Edit Tools Units help inr'o AVL • • • • B B I advisor 7004 160 100 50 . - A ss" J " \ , - cyc_kph_r • kpha / \ / / Li i LL 100 \ / vyv \ / 1 fc fuel rate i y w iji /i 11 - he co/10 • nox - pm R e s u l t s f igure C o m p o n e n t fuel .converter Plot Variable (Select Axis First) Fuel Consumption (L/100 km) Gasoline Equivalent Distance (km) 10.7 10.7 12.9 E m i s s i o n s ( g r a m s / k m ) ] standards HC CO NOx PM ?.486 0X39 0 1.759 A c c e l e r a t i o n T o s t 0-96.6 km/h (s). n/a 54 .4-96.6 km/h (e): n/a 0-136.8 krn/h(s): n/a Max. Accel. (m/cA2): n/a Distance in 5s (m): n/a Time in 0.4km (s): n/a Max Speed (kmph): n/a n/a % Energy Use Figure Output Check Plots Compare Results With. j ' "sim Data j | Test Data Wai rungs/Messages Figure 6.35: Results of Conventional vehicle in US 06 300 250 200 150 100 50 0 -50 - 1 0 0 -150 500 max torque curve operating points(gear 1) O operating points (gear 2) 0 operating points(gear 3) operating points(gear 4) operating points(gear 5) downshift 2->1 downshift 3->2 downshift 4->3 downshift 5->4 upshift 1->2 upshift 2->3 upshift 3->4 upshift 4->5 1000 1500 2000 2500 3000 3500 4000 Speed (rpm) 300 250 200 150 ? z 100 1 K 50 t— 0 -50 -100 -150 • f 'St V 'y V . . 30.6 * -t- , 'i 'jfi.i - - max torque cur\e output shaft op. pts(includes inertia & accessories) 500 1000 1500 2000 2500 3000 3500 4000 Speed (rpm) Difference between requested and achieved speeds 0.4 0.35 0.3 0.25 0 .2 0 15 0 .1 0 05 0 Fuel Convener Efficiency 300 time (s) Figure 6.24: Motor efficiency of Conventional vehicle in CDC 1U 78 6.5 Conclusion After analyzing all the simulation results and collected data during the study following conclusions can be made. • The optimization is really effective in road conditions which shows very irregular variation in speed vs. time o ex: toyota prius in sri lankan drive cycle • Optimized hybrids are more economical in irregularly varying conditions than in highways. • To maximize the effectiveness of concept hybrid, the sudden changes in power demand is essential. • It is important to analyze the fuel economy of vehicles using transient drive cycles such as cdc as they are behaving differently compared to model drive cycles. • Series hybrids performing worse than conventional vehicles in such drive cycles due to many energy conversions 79 References: [1] E. Cerruto, A. Consoli, A. Raciti, A. Testa "Energy Flow Management in Hybrid Vehicle by Fuzzy Logic Controller," University di Ctania, Viale Andrea Doria, 6 95125, Catania Italy. [2] B. M Baumann, G. Washington, B. C. Glenn and G. Rizzani, " Mechatronic Design and Control of Hybrid Electric Vehicles," IEEE/ASME Trans, on Mechatronics, Vol. 5, no 1, March 2000. [3] L. Wang, "Hybrid Electric Vehicle Design Based On A Multi-Objective Optimization Evolutionary Algorithm," W. J. Karplus Summer Research Grant Report 2005, Department of Electrical and Computer Engineering, Texas AandM University College Station, Texas 77843. [4] R.Graham, "Comparing the Benefits and Impacts of HEV Options,"EPRI,Palo Alto, CA,2001.1000349. [5] B. Bagot and O. Lindblad, "Uncovering the True Potential of Hybrid Electric Vehicles," Msc International Business Masters Thesis No 2004:12, Graduate Business School, School of Economics and Commercial Law, Goteborg University, ISSN 1403-85IX. [6] New York City Taxi and Limousine Commission home page, "Hybrid Electric Vehicles Cost/Benefit Overview," http:// www.nyc.gov html /tic /html /home /home.shtml, September 8, 2005. [7] R. H. Staunton, S. C. Nelson, P. J. Otaduy, J. W. McKeever, J. M. Bailey, S. Das and R. L. Smith, "PM Motor Parametric Design Analyses for a Hybrid Electric Vehicle Traction Drive Application— Final Report, " Engineering Science and Technology Division, OAK RIDGE NATIONAL LABORATORY, Oak Ridge, Tennessee 37831, September 2004. 80 D. Corrigan, I. Menjak, B. Cleto, S. Dhar and S. Ovshinsky,"Nickel-Metal Hydride Batteries For ZEV-Range Hybrid Electric Vehicles," Ovonic Battery Company, Troy, Michigan, USA. G.J. Su and J. W. McKeever, "Design of a PM Brushless Motor Drive for Hybrid Electrical Vehicle Application," PCIM 2000, Boston. MA, October 1- 5, 2000. H. Hamada, S. Yoshihara and H. Hamano, "Development of Fuel-efficient, Environmentally-friendly Hybrid Electric Vehicle Systems," Hitachi Review Vol. 53 (2004), No. 4, pp 177-181. Y. Muragishi and E. Ono, "Application of Hybrid Control Method to Braking Control System with Estimation of Tire Force Characteristics," RandD Review of Toyota, CRDL Vol. 38, No. 2, pp22-30. N. J. Schouten, Mutasim A. Salman and N. A. Kheir, "Fussy Logic Control for Parellel Hybrid Vehicles," IEEE Trans, on Control Tech. vol. 10, no. 3, May 2002. J. S. Won and P. Langari " Intelegent Energy Management Agent for a Parellel Hybrid Vehicle - Parti : System Architecture and Design of the Driving Situation Identification Process," IEEE Trans, on Vehi. Tech., vol. 5, no. 3, May 2005, pp 925-934. J. S. Won and P. Langari " Intelegent Energy Management Agent for a Parellel Hybrid Vehicle - Partll : Torque Distribution, Charge Sustenance Strategies, and Performance Results ," IEEE Trans, on Vehi. Tech., vol. 54, no. 3, May 2005, pp 935-953. C. Manzie, H. Watson, S. Halgamuge. "Fuel Economy Improvement for Urban Driving Hybrid vs Intelligent Vehicles," Transportation Research C 15(2007) pp. 1-16, University of Melbourne 81 [16] M. Montazeri, and A. Poursamad, "Application of genetic algorithm for simultaneous optimization of HEV component sizing and control strategy," Int. J. Alternative Propulsion, Vol. 1, No. 1, 2006, pp 63-78. [17] G.T. Pulido and C. A. Coello Coello, "The Micro Genetic Algorithm 2: Towards On-Line Adaptation in Evolutionary Multiobjective Optimization," CINVESTAV-IPN, Evolutionary Computation Group, Depto. de Ingenier'ia El'ectrica, Secci'on de Computaci'on, Av. Instituto Polit'ecnico Nacional No. 2508, Col. San Pedro Zacatenco, M'exico, D. F. 07300. [18] Y. L. Zhou, "Modeling and Simulation of Hybrid Electric Vehicles," Master Thesis, University of Science and Tech, Beiging - 2005. [19] "Hybrid Synergy Drives- Toyota Hybrid Systems," Toyota Motor Corporation, Public Affairs Division, 4-8 Koraku 1-chome, Bunkyo-ku, Tokyo, 112-8701 Japan May 2003. [20] Toyota Prius User-Guide, Third Edition, First Revision for the HSD model (2004 and 2005- last Updated on 8/20/2005) [21] K.F.Egeback and S.Bucksch, " Hybrid Electric Vehicles. An Alternative for the Swidish Market?," KFB-Report, 2000:53, October 2000. [22] "Hybrid Electric Drive Heavy Duty Vehicle Testing Project - Final Emissions Report," Northeast Advanced Vehicle Consortium M. J. Bradley and Associates, Inc. West Virginia University, Feb. 2000. [23] D. A. Niemeier, T. Limanond and J. E. Morey, "Data Collection for Driving Cycles Development : Evaluation of Data Collection Protocol, Final, October 1999," Department of Civil and Environmental Engineering, Institute of Transportation, University of California, Davis. [24] "Freedom CAR & Vehicle Technologies Program," Publication of Department of Energy, U.S. A, January 2004. Appendix A: Published research papers Determination of Maximum Possible Fuel Economy of HEV for Known Drive Cycle: Genetic Algorithm Based Approach R. Suda th Wimalendra* , L a n k a Udawat ta" , E. M. C. P. Ed i r i s inghe and S u d a r s h a n a Karuna ra thna Department of Electrical Engineering, Faculty of Engineering University of Moratuwa, Sri Lanka Emails: rs wimalendraiir/'vahoo.com. +Email: lankafmieee.oru Abstract—This paper describes a methodological approach to investigate the maximum fuel economy that could be achieved by a hybrid vehicle with parallel configuration for a known drive cycle. A backward looking hybrid vehicle model is used for computation of fuel economics. The optimization process represents a constrained, multi-domain and time-varying problem, which is highly nonlinear. Here, genetic algorithm (GA) based approach was used to find out optimum power split between two power sources over their driving cycles that make maximum possible overall fuel economy for the given drive cycle by the vehicle. In this approach using Parallel Hybrid Electric Vehicle (I'll EV) configuration, optimization problem is formulated so as to minimize the overall fuel consumption. The whole set of clectric motor power contribution along the drive cycle is then coded as the chromosomes. These results represent the maximum fuel economy that could be ever achieved by any power management system of a Hybrid Electric Vchiclc, with the tested HEV configuration and shall allow setting a benchmark against which the fuel economy is measured. Keywords-— Hybrid Electric Vehicles, Optimization, Genetic Algorithm I. INTRODUCTION As a result of the endless interest of the society for improved fuel economy & reduced emission without sacrificing vehicle performance, safety, reliability, cost of ownership and other conventional vehicle attributes, Hybrid Technology came in to the world of automobiles, leaving lot of research topics to the researchers living all over the globe. The pressing environmental concerns and skyrocketing price ii!' fuel oils are highly responsible factors for the rapid development of this technology within the past two decades. Hybrid Electric Vehicles (HEV) have a great potential as new alternative means of transportation. The specific benefits of HEVs, compared to conventional vehicles, include improved fuel economy and reduced emissions. Combustion Engine in the optimum operating range while making use of regenerative braking during deceleration. An extensive set of studies have been conducted over the past two decades. In particular, several logic-based control strategies and fuzzy logic-based energy management strategics for distributing power demand have been suggested [1], [2] & [3]. These approaches have been adopted mainly due to their effectiveness in dealing with the problems appear in the complexity of hybrid drive train via both heuristics (human expertise) and mathematical models. Recent changcs in the technology of modern vehicles and revolutionary development in telematics industry have created the possibility for a vehicle to gather online information about the road infrastructure and the traffic environment in which it is in operation. Several algorithms have been proposed to predict the future speed trends, with the use of preview information provided by the telematics. Two technologies, hybrid and telematics are combined together to create "intelligent vehicle" which provides improved fuel economy with traffic preview [4]. The aim of this study is to find out the maximum fuel economy that a PHEV can achieve with any type of HEV energy management system. Here, genetic algorithm (GA) has beeH used as the technique for optimization which will lead to find a global optimum. In fact, though it is needed to find the maximum possible theoretical best, in actual practice it might not be reachable. However, knowing the maximum possible best fuel economy, it can be used as a benchmark value which might be useful in setting the standards of HEV. Rest of the paper has been organized as follows; In Section II, it explains the vehicle model used in this study and briefly describes the driving cycle used. Evolutionary computational algorithm to find out the maximum fuel economy has been presented in Section III, followed by the analysis of the results of this study in Section IV. Finally, the Conclusion is presented in Section V. ,1 lybrid systems involving a combination of an Internal Combustion Engine (ICE) and elcctric motors (EM) have the potential of improving fuel economy, by operating the Internal 9 7 8 - 1 - 4 2 4 4 - 2 9 0 0 - 4 / 0 8 / 5 2 5 . 0 0 © 2 0 0 8 I E E E I C I A F S 0 8 II. VEHICLE MODEL AND DRIVE CYCLE .'/. Modeling the hybrid vehicle A parallel hybrid configuration has been taken in Fig. 1 to account for modeling the hybrid electric vehicle in this study. This configuration consists of an electric motor and internal combustion engine that can simultaneously or individually drive the transmission (and subsequently propel the vehicle), f h e split is determined by the vehicle's hybrid control strategy [subject to constraints on the battery state of charge (SOC)]. Normally, the EM is used to assists the engine for peak acceleration, hill climbing, and extremely fast highway driving conditions. Furthermore, the EM can act in reverse mode to become a generator during regenerative braking and consequently used to recharge the batteries. Fig. I B lock d i a g r a m o l ' t h e paral le l hybr id v c h i d c The city driving test standard starts from ambient initial conditions (known as "cold starts"). However, in this analysis, the engine has been assumed to be warm. Another constraint during the optimization process is the change in, state of battery charge at the beginning and end of the cycle in order to prevent misleading fuel economy results, arising from excessive use of the electric motor (this would result increased fuel usage during the next vehicle run, for battery replenishment). The baseline vehicle chosen for this study has been a 4 - 1 production family sedan with a specific Parallel Hybrid Electric Vehicle (PHEV) configuration, which has been used throughout the study. Following table gives details of its' specifications; T A B L E 1 V E H I C L E M O D E L S P E C I F I C A T I O N S 'ammeter Value Total weight Chassis weight Frontal area Coefficient of Drag Vehicle length Transmission 1642 kg 1000 kg 1.92 m2 0.32 5.00 m Manual, 5 speed Transmission efficiency Gear ratios Final drive ratio Gear changes 95% (all gears) 3.5:2.14:1.39:1:0.78 .3.98 1 - 2 and 2 - 1 @ 2£ ZHE ZOZ 00 L'6f L SZ 0 Of 69Z O'Efr E SZ 9"E£ S'OE LOZ 00 I OS 8 S3 6'6£ 89Z 6'£f S SZ KZE EHE OOZ 00 6'6f S 92 Z 6E Z9Z Zt't' ZSZ LIE 0 ZE 661 90 Z'6f 0'92 8 6E 992 f 8>Z f'OE SEE 861 LH 6 6f SS2 86E S92 0"8t' 9>2 9'6Z 6>E Z6L 90 LOS O'SZ Z'6£ f92 t'8f t7fZ 6'8Z 0'9E 96 L 00 9 6f s>z S'8E E9Z LSt? t' 22 L '82 O'ZE S6L 00 L'6f 9 fZ f'9E 292 6 8f t'OZ L'82 Z8E f6L 00 6'8f 9'fZ Z'PZ L92 Z'6fr Z 81 L'8Z E OP £61 OH L8P 9 fZ 9'ZE 09Z 60S 0'9L L'8Z 8 IP Z6L 61 68P 9'fZ 6'0E 6SZ L'2S S'9L 0'8Z Z'ZP L6L 01 L'6f ZSZ S'OE 8SZ L'2S O'ZL L 9Z PZP 06L 0 0 E8f 8 SZ O'OE ZSZ L'2S Z'9L Z>Z 8ZP 68L 0 0 YLP f9Z 0 62 9SZ L'2S E'SL 9'EZ LEV 88L 0 0 9 9f OZZ 6'ZZ SS2 L'ZS Z'EL O'EZ LZP Z8L so Z'Sf Z'9Z I 92 fS2 9 ZS OIL PZZ PZP 981 OH f'ff f'SZ E'fZ ES2 L ES Z'OL ZLZ I'ZP S8L 9 2 i£f 9PZ EHZ 2S2 L ES E'OL 9H2 OZP P81 Z'f S'Lf 8 EZ E'8L LS2 L'ES E'OL t'LZ 6Hf est OP 8 6E LIZ 6'ZL 0S2 fr ZS E'OL 2 L2 OZP 28L 8'E 0'6E 9'6L f ZL 6f2 Z'LS S 6 OLZ OZP L8L EE L'8£ S ZL 8 6L 8f2 8HS L'8 8 ZZ PZP 081 LZ t'SE f SL ZZZ ZfZ 8 LS t>9 9'PZ LZP 6ZL 9'Z zof 6'EL 8'0Z 9f2 9HS Of Z'9Z S'Zf 8ZL S'Z LZP f'ZL £61 St72 frHS LZ 6 ZZ Z'ZP ZZL SZ I'PP 6 01 VIZ t-t-2 80S P L 8'8Z Z'ZP 9ZL sz 6'ff f6 6'fZ EfrZ LOS LH Z'6Z Z'ZP SZL SH O'SP Z'6 f'62 2^2 S'8fr Z'O 9'OE 8'ZP PL L SO 2'St' 0 6 6'EE Lt?2 8 9^ SO t'HE Z'ZP EZL CO f'Sf 88 9'ZE OPZ CO 8HE 8'ZP ZZL 0 0 L'9f 98 ZHf 6EZ SO L ZE 9'ZP LZL EO Z'9f 16 Z'Zf 8EZ t'Ot-Z'O S'ZE 8'ZP OZL SO 6 St-9 6 2 Ef ZEZ t'SE P'O 8'ZE Z'ZP 691 tH 0 St' tot S'Lf 9EZ 8'9£ 0 0 L EE OPP 89 L Z'L f'ff 9 01 8'6E SEZ L'SE 00 E'EE 9'PP Z9L Z'L ZEf 6Ht 0'6E KZ 8>£ 00 9'EE L'Sfr 99 L Z'O f Ef Z EL L'8E EEZ t>>£ 00 8'EE 9 8P S9L 6 0 O'Ef S f I L'8E ZEZ f'6E 00 E'fE 88P f9L LH 61 Z'Ef ZSL L 8E LEZ VVV EO LP E 6'Sf £91 fEf LSI E'SE OEZ 9 8V so L'SE 6 Sf Z9L LZ rot' f'f L f'8£ 6ZZ L'ZS Z'O S SE 8'Sf L9L Sf Z 9E Z'EL f'8E 8ZZ 6'ZS 60 8'SE LP P 09 L E'9 L'fE O'EL f SE ZZZ L'ES Z'O 0'9E 8ZP 6SL SOI f HE 8LL E 8E 9ZZ 6 ES so Z 9E 8'ZP 8SL Z'fl CLE 9 01 L 8E SZZ z>s 90 P9Z OZP ZSL 902 LIE f 6 S'9E t-ZZ Z'SS Z'O ZLZ Z'L f 9SL f'92 9'0E Z 8 6 f£ EZZ Z'SS OH 0'8E EHf SSL 8'0E O'OE L'8 Z'SE ZZZ 9'SS Z'L 8'8E E'Lf PS I L'SE 082 6 Z f'9E LZZ fr'SS ZH S'6E ZIP ESL E'SE 0 92 8 Z 9 ZE OZZ fr'SS LH 0 6E 9 IP ZSL t'SE O'SZ 9'Z 8'8E 6LZ fr'SS EH P8Z OZP LSL L'SE O'fZ ELL S'ZE 8LZ fr >s SH 6'ZE ZZP OSL Z'f E O'SZ 0 SL 2 9£ ZLZ t? 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