Internal Combustion Engines
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Converting an Internal Combustion Engine Vehicle to an Electric Vehicle
AC 2011-1048: CONVERTING AN INTERNAL COMBUSTION ENGINE VEHICLE TO AN ELECTRIC VEHICLE Ali Eydgahi, Eastern Michigan University Dr. Eydgahi is an Associate Dean of the College of Technology, Coordinator of PhD in Technology program, and Professor of Engineering Technology at the Eastern Michigan University. Since 1986 and prior to joining Eastern Michigan University, he has been with the State University of New York, Oak- land University, Wayne County Community College, Wayne State University, and University of Maryland Eastern Shore. Dr. Eydgahi has received a number of awards including the Dow outstanding Young Fac- ulty Award from American Society for Engineering Education in 1990, the Silver Medal for outstanding contribution from International Conference on Automation in 1995, UNESCO Short-term Fellowship in 1996, and three faculty merit awards from the State University of New York. He is a senior member of IEEE and SME, and a member of ASEE. He is currently serving as Secretary/Treasurer of the ECE Division of ASEE and has served as a regional and chapter chairman of ASEE, SME, and IEEE, as an ASEE Campus Representative, as a Faculty Advisor for National Society of Black Engineers Chapter, as a Counselor for IEEE Student Branch, and as a session chair and a member of scientific and international committees for many international conferences. Dr. Eydgahi has been an active reviewer for a number of IEEE and ASEE and other reputedly international journals and conferences. He has published more than hundred papers in refereed international and national journals and conference proceedings such as ASEE and IEEE. Mr. Edward Lee Long IV, University of Maryland, Eastern Shore Edward Lee Long IV graduated from he University of Maryland Eastern Shore in 2010, with a Bachelors of Science in Engineering. -
Physics 170 - Thermodynamic Lecture 40
Physics 170 - Thermodynamic Lecture 40 ! The second law of thermodynamic 1 The Second Law of Thermodynamics and Entropy There are several diferent forms of the second law of thermodynamics: ! 1. In a thermal cycle, heat energy cannot be completely transformed into mechanical work. ! 2. It is impossible to construct an operational perpetual-motion machine. ! 3. It’s impossible for any process to have as its sole result the transfer of heat from a cooler to a hotter body ! 4. Heat flows naturally from a hot object to a cold object; heat will not flow spontaneously from a cold object to a hot object. ! ! Heat Engines and Thermal Pumps A heat engine converts heat energy into work. According to the second law of thermodynamics, however, it cannot convert *all* of the heat energy supplied to it into work. Basic heat engine: hot reservoir, cold reservoir, and a machine to convert heat energy into work. Heat Engines and Thermal Pumps 4 Heat Engines and Thermal Pumps This is a simplified diagram of a heat engine, along with its thermal cycle. Heat Engines and Thermal Pumps An important quantity characterizing a heat engine is the net work it does when going through an entire cycle. Heat Engines and Thermal Pumps Heat Engines and Thermal Pumps Thermal efciency of a heat engine: ! ! ! ! ! ! From the first law, it follows: Heat Engines and Thermal Pumps Yet another restatement of the second law of thermodynamics: No cyclic heat engine can convert its heat input completely to work. Heat Engines and Thermal Pumps A thermal pump is the opposite of a heat engine: it transfers heat energy from a cold reservoir to a hot one. -
Internal and External Combustion Engine Classifications: Gasoline
Internal and External Combustion Engine Classifications: Gasoline and diesel engine classifications: A gasoline (Petrol) engine or spark ignition (SI) burns gasoline, and the fuel is metered into the intake manifold. Spark plug ignites the fuel. A diesel engine or (compression ignition Cl) bums diesel oil, and the fuel is injected right into the engine combustion chamber. When fuel is injected into the cylinder, it self ignites and bums. Preparation of fuel air mixture (gasoline engine): * High calorific value: (Benzene (Gasoline) 40000 kJ/kg, Diesel 45000 kJ/kg). * Air-fuel ratio: A chemically correct air-fuel ratio is called stoichiometric mixture. It is an ideal ratio of around 14.7:1 (14.7 parts of air to 1 part fuel by weight). Under steady-state engine condition, this ratio of air to fuel would assure that all of the fuel will blend with all of the air and be burned completely. A lean fuel mixture containing a lot of air compared to fuel, will give better fuel economy and fewer exhaust emissions (i.e. 17:1). A rich fuel mixture: with a larger percentage of fuel, improves engine power and cold engine starting (i.e. 8:1). However, it will increase emissions and fuel consumption. * Gasoline density = 737.22 kg/m3, air density (at 20o) = 1.2 kg/m3 The ratio 14.7 : 1 by weight equal to 14.7/1.2 : 1/737.22 = 12.25 : 0.0013564 The ratio is 9,030 : 1 by volume (one liter of gasoline needs 9.03 m3 of air to have complete burning). -
Robosuite: a Modular Simulation Framework and Benchmark for Robot Learning
robosuite: A Modular Simulation Framework and Benchmark for Robot Learning Yuke Zhu Josiah Wong Ajay Mandlekar Roberto Mart´ın-Mart´ın robosuite.ai Abstract robosuite is a simulation framework for robot learning powered by the MuJoCo physics engine. It offers a modular design for creating robotic tasks as well as a suite of benchmark environments for reproducible re- search. This paper discusses the key system modules and the benchmark environments of our new release robosuite v1.0. 1 Introduction We introduce robosuite, a modular simulation framework and benchmark for robot learning. This framework is powered by the MuJoCo physics engine [15], which performs fast physical simulation of contact dynamics. The overarching goal of this framework is to facilitate research and development of data-driven robotic algorithms and techniques. The development of this framework was initiated from the SURREAL project [3] on distributed reinforcement learning for robot manipulation, and is now part of the broader Advancing Robot In- telligence through Simulated Environments (ARISE) Initiative, with the aim of lowering the barriers of entry for cutting-edge research at the intersection of AI and Robotics. Data-driven algorithms [9], such as reinforcement learning [13,7] and imita- tion learning [12], provide a powerful and generic tool in robotics. These learning arXiv:2009.12293v1 [cs.RO] 25 Sep 2020 paradigms, fueled by new advances in deep learning, have achieved some excit- ing successes in a variety of robot control problems. Nonetheless, the challenges of reproducibility and the limited accessibility of robot hardware have impaired research progress [5]. In recent years, advances in physics-based simulations and graphics have led to a series of simulated platforms and toolkits [1, 14,8,2, 16] that have accelerated scientific progress on robotics and embodied AI. -
Toward Multi-Engine Machine Translation Sergei Nirenburg and Robert Frederlcing Center for Machine Translation Carnegie Mellon University Pittsburgh, PA 15213
Toward Multi-Engine Machine Translation Sergei Nirenburg and Robert Frederlcing Center for Machine Translation Carnegie Mellon University Pittsburgh, PA 15213 ABSTRACT • a knowledge-based MT (K.BMT) system, the mainline Pangloss Current MT systems, whatever translation method they at present engine[l]; employ, do not reach an optimum output on free text. Our hy- • an example-based MT (EBMT) system (see [2, 3]; the original pothesis for the experiment reported in this paper is that if an MT idea is due to Nagao[4]); and environment can use the best results from a variety of MT systems • a lexical transfer system, fortified with morphological analysis working simultaneously on the same text, the overallquality will im- and synthesis modules and relying on a number of databases prove. Using this novel approach to MT in the latest version of the - a machine-readable dictionary (the Collins Spanish/English), Pangloss MT project, we submit an input text to a battery of machine the lexicons used by the KBMT modules, a large set of user- translation systems (engines), coLlect their (possibly, incomplete) re- generated bilingual glossaries as well as a gazetteer and a List sults in a joint chaR-like data structure and select the overall best of proper and organization names. translation using a set of simple heuristics. This paper describes the simple mechanism we use for combining the findings of the various translation engines. The results (target language words and phrases) were recorded in a chart whose initial edges corresponded to words in the source language input. As a result of the operation of each of the MT 1. -
HCM1A0503 Inductor Data Sheet
Technical Data 10547 Effective August 2016 HCM1A0503 Automotive grade High current power inductors Applications • Body electronics • Central body control module • Vehicle access control system • Headlamps, tail lamps and interior lighting • Heating ventilation and air conditioning controllers (HVAC) • Doors, window lift and seat control • Advanced driver assistance systems • 77 GHz radar system • Basic and smart surround, and rear and front view camera • Adaptive cruise control (ACC) Product features • Automatic parking control • AEC-Q200 Grade 1 qualified • Collision avoidance system/Car black box system • High current carrying capacity • Infotainment and cluster electronics • Magnetically shielded, low EMI • Active noise cancellation (ANC) • Frequency range up to 1 MHz • Audio subsystem: head unit and trunk amp • Inductance range from 0.2 μH to 10 μH • Digital instrument cluster • Current range from 2.3 A to 24 A • In-vehicle infotainment (IVI) and navigation • 5.5 mm x 5.3 mm footprint surface mount package in a 3.0 mm height • Chassis and safety electronics • Alloy powder core material • Airbag control unit • Moisture Sensitivity Level (MSL): 1 • Engine and Powertrain Systems • Halogen free, lead free, RoHS compliant • Powertrain control module (PCU)/Engine Control unit (ECU) • Transmission Control Unit (TCU) Environmental Data • Storage temperature range (Component): -55 °C to +155 °C • Operating temperature range: -55 °C to +155 °C (ambient plus self-temperature rise) • Solder reflow temperature: J-STD-020 (latest revision) compliant -
Exploring Design, Technology, and Engineering
Exploring Design, Technology, & Engineering © 2012 Chapter 21: Energy-Conversion Technology—Terms and Definitions active solar system: a system using moving parts to capture and use solar energy. It can be used to provide hot water and heating for homes. anode: the negative side of a battery. cathode: the positive side of a battery. chemical converter: a technology that converts energy in the molecular structure of substances to another energy form. conductor: a material or object permitting an electric current to flow easily. conservation: making better use of the available supplies of any material. electricity: the movement of electrons through materials called conductors. electromagnetic induction: a physical principle causing a flow of electrons (current) when a wire moves through a magnetic field. energy converter: a device that changes one type of energy into a different energy form. external combustion engine: an engine powered by steam outside of the engine chambers. fluid converter: a device that converts moving fluids, such as air and water, to another form of energy. four-cycle engine: a heat engine having a power stroke every fourth revolution of the crankshaft. fuel cell: an energy converter that converts chemicals directly into electrical energy. gas turbine: a type of engine that creates power from high velocity gases leaving the engine. heat engine: an energy converter that converts energy, such as gasoline, into heat, and then converts the energy from the heat into mechanical energy. internal combustion engine: a heat engine that burns fuel inside its cylinders. jet engine: an engine that obtains oxygen for thrust. mechanical converter: a device that converts kinetic energy to another form of energy. -
A Two-Point Vehicle Classification System
178 TRANSPORTATION RESEARCH RECORD 1215 A Two-Point Vehicle Classification System BERNARD C. McCULLOUGH, JR., SrAMAK A. ARDEKANI, AND LI-REN HUANG The counting and classification of vehicles is an important part hours required, however, the cost of such a count was often of transportation engineering. In the past 20 years many auto high. To offset such costs, many techniques for the automatic mated systems have been developed to accomplish that labor counting, length determination, and classification of vehicles intensive task. Unfortunately, most of those systems are char have been developed within the past decade. One popular acterized by inaccurate detection systems and/or classification method, especially in Europe, is the Automatic Length Indi methods that result in many classification errors, thus limiting cation and Classification Equipment method, known as the accuracy of the system. This report describes the devel "ALICE," which was introduced by D. D. Nash in 1976 (1). opment of a new vehicle classification database and computer This report covers a simpler, more accurate system of vehicle program, originally designed for use in the Two-Point-Time counting and classification and details the development of the Ratio method of vehicle classification, which greatly improves classification software that will enable it to surpass previous the accuracy of automated classification systems. The program utilizes information provided by either vehicle detection sen systems in accuracy. sors or the program user to determine the velocity, number Although many articles on vehicle classification methods of axles, and axle spacings of a passing vehicle. It then matches have appeared in transportation journals within the past dec the axle numbers and spacings with one of thirty-one possible ade, most have dealt solely with new types, or applications, vehicle classifications and prints the vehicle class, speed, and of vehicle detection systems. -
Revisiting the Compcars Dataset for Hierarchical Car Classification
sensors Article Revisiting the CompCars Dataset for Hierarchical Car Classification: New Annotations, Experiments, and Results Marco Buzzelli * and Luca Segantin Department of Informatics Systems and Communication, University of Milano-Bicocca, 20126 Milano, Italy; [email protected] * Correspondence: [email protected] Abstract: We address the task of classifying car images at multiple levels of detail, ranging from the top-level car type, down to the specific car make, model, and year. We analyze existing datasets for car classification, and identify the CompCars as an excellent starting point for our task. We show that convolutional neural networks achieve an accuracy above 90% on the finest-level classification task. This high performance, however, is scarcely representative of real-world situations, as it is evaluated on a biased training/test split. In this work, we revisit the CompCars dataset by first defining a new training/test split, which better represents real-world scenarios by setting a more realistic baseline at 61% accuracy on the new test set. We also propagate the existing (but limited) type-level annotation to the entire dataset, and we finally provide a car-tight bounding box for each image, automatically defined through an ad hoc car detector. To evaluate this revisited dataset, we design and implement three different approaches to car classification, two of which exploit the hierarchical nature of car annotations. Our experiments show that higher-level classification in terms of car type positively impacts classification at a finer grain, now reaching 70% accuracy. The achieved performance constitutes a baseline benchmark for future research, and our enriched set of annotations is made available for public download. -
Modelling of Emissions and Energy Use from Biofuel Fuelled Vehicles at Urban Scale
sustainability Article Modelling of Emissions and Energy Use from Biofuel Fuelled Vehicles at Urban Scale Daniela Dias, António Pais Antunes and Oxana Tchepel * CITTA, Department of Civil Engineering, University of Coimbra, Polo II, 3030-788 Coimbra, Portugal; [email protected] (D.D.); [email protected] (A.P.A.) * Correspondence: [email protected] Received: 29 March 2019; Accepted: 13 May 2019; Published: 22 May 2019 Abstract: Biofuels have been considered to be sustainable energy source and one of the major alternatives to petroleum-based road transport fuels due to a reduction of greenhouse gases emissions. However, their effects on urban air pollution are not straightforward. The main objective of this work is to estimate the emissions and energy use from bio-fuelled vehicles by using an integrated and flexible modelling approach at the urban scale in order to contribute to the understanding of introducing biofuels as an alternative transport fuel. For this purpose, the new Traffic Emission and Energy Consumption Model (QTraffic) was applied for complex urban road network when considering two biofuels demand scenarios with different blends of bioethanol and biodiesel in comparison to the reference situation over the city of Coimbra (Portugal). The results of this study indicate that the increase of biofuels blends would have a beneficial effect on particulate matter (PM ) emissions reduction for the entire road network ( 3.1% [ 3.8% to 2.1%] by kg). In contrast, 2.5 − − − an overall negative effect on nitrogen oxides (NOx) emissions at urban scale is expected, mainly due to the increase in bioethanol uptake. Moreover, the results indicate that, while there is no noticeable variation observed in energy use, fuel consumption is increased by over 2.4% due to the introduction of the selected biofuels blends. -
Making Markets for Hydrogen Vehicles: Lessons from LPG
Making Markets for Hydrogen Vehicles: Lessons from LPG Helen Hu and Richard Green Department of Economics and Institute for Energy Research and Policy University of Birmingham Birmingham B15 2TT United Kingdom Hu: [email protected] Green: [email protected] +44 121 415 8216 (corresponding author) Abstract The adoption of liquefied petroleum gas vehicles is strongly linked to the break-even distance at which they have the same costs as conventional cars, with very limited market penetration at break-even distances above 40,000 km. Hydrogen vehicles are predicted to have costs by 2030 that should give them a break-even distance of less than this critical level. It will be necessary to ensure that there are sufficient refuelling stations for hydrogen to be a convenient choice for drivers. While additional LPG stations have led to increases in vehicle numbers, and increases in vehicles have been followed by greater numbers of refuelling stations, these effects are too small to give self-sustaining growth. Supportive policies for both vehicles and refuelling stations will be required. 1. Introduction While hydrogen offers many advantages as an energy vector within a low-carbon energy system [1, 2, 3], developing markets for hydrogen vehicles is likely to be a challenge. Put bluntly, there is no point in buying a vehicle powered by hydrogen, unless there are sufficient convenient places to re-fuel it. Nor is there any point in providing a hydrogen refuelling station unless there are vehicles that will use the facility. What is the most effective way to get round this “chicken and egg” problem? Data from trials of hydrogen vehicles can provide information on driver behaviour and charging patterns, but extrapolating this to the development of a mass market may be difficult. -
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