Real Time Autonomous Cruise Control Of Connected Plug In Hybrid Electric Vehicles Under Uncertainty
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| Author | : Amir Taghavipour |
| Publisher | : Springer |
| Total Pages | : 202 |
| Release | : 2018-09-26 |
| Genre | : Technology & Engineering |
| ISBN | : 3030003140 |
Intelligent Control of Connected Plug-in Hybrid Electric Vehicles presents the development of real-time intelligent control systems for plug-in hybrid electric vehicles, which involves control-oriented modelling, controller design, and performance evaluation. The controllers outlined in the book take advantage of advances in vehicle communications technologies, such as global positioning systems, intelligent transportation systems, geographic information systems, and other on-board sensors, in order to provide look-ahead trip data. The book contains simple and efficient models and fast optimization algorithms for the devised controllers to address the challenge of real-time implementation in the design of complex control systems. Using the look-ahead trip information, the authors of the book propose intelligent optimal model-based control systems to minimize the total energy cost, for both grid-derived electricity and fuel. The multilayer intelligent control system proposed consists of trip planning, an ecological cruise controller, and a route-based energy management system. An algorithm that is designed to take advantage of previewed trip information to optimize battery depletion profiles is presented in the book. Different control strategies are compared and ways in which connecting vehicles via vehicle-to-vehicle communication can improve system performance are detailed. Intelligent Control of Connected Plug-in Hybrid Electric Vehicles is a useful source of information for postgraduate students and researchers in academic institutions participating in automotive research activities. Engineers and designers working in research and development for automotive companies will also find this book of interest. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
| Author | : Muhammad H. Rashid |
| Publisher | : Elsevier |
| Total Pages | : 370 |
| Release | : 2024-07-22 |
| Genre | : Technology & Engineering |
| ISBN | : 0323950981 |
Handbook of Power Electronics in Autonomous and Electric Vehicles provides advanced knowledge on autonomous systems, electric propulsion in electric vehicles, radars and sensors for autonomous systems, and relevant aspects of energy storage and battery charging. The work is designed to provide clear technical presentation with a focus on commercial viability. It supports any and all aspects of a project requiring specialist design, analysis, installation, commissioning and maintenance services. With this book in hand, engineers will be able to execute design, analysis and evaluation of assigned projects using sound engineering principles and commercial requirements, policies, and product and program requirements. - Presents core power systems and engineering applications relevant to autonomous and electric vehicles in characteristic depth and technical presentation - Offers practical support and guidance with detailed examples and applications for laboratory vehicular test plans and automotive field experimentation - Includes modern technical coverage of emergent fields, including sensors and radars, battery charging and monitoring, and vehicle cybersecurity
| Author | : Simona Onori |
| Publisher | : Springer |
| Total Pages | : 121 |
| Release | : 2015-12-16 |
| Genre | : Technology & Engineering |
| ISBN | : 1447167813 |
This SpringerBrief deals with the control and optimization problem in hybrid electric vehicles. Given that there are two (or more) energy sources (i.e., battery and fuel) in hybrid vehicles, it shows the reader how to implement an energy-management strategy that decides how much of the vehicle’s power is provided by each source instant by instant. Hybrid Electric Vehicles: •introduces methods for modeling energy flow in hybrid electric vehicles; •presents a standard mathematical formulation of the optimal control problem; •discusses different optimization and control strategies for energy management, integrating the most recent research results; and •carries out an overall comparison of the different control strategies presented. Chapter by chapter, a case study is thoroughly developed, providing illustrative numerical examples that show the basic principles applied to real-world situations. The brief is intended as a straightforward tool for learning quickly about state-of-the-art energy-management strategies. It is particularly well-suited to the needs of graduate students and engineers already familiar with the basics of hybrid vehicles but who wish to learn more about their control strategies.
| Author | : D. T. Pham |
| Publisher | : Springer Nature |
| Total Pages | : 413 |
| Release | : |
| Genre | : |
| ISBN | : 3031649362 |
| Author | : James M. Anderson |
| Publisher | : Rand Corporation |
| Total Pages | : 215 |
| Release | : 2014-01-10 |
| Genre | : Transportation |
| ISBN | : 0833084372 |
The automotive industry appears close to substantial change engendered by “self-driving” technologies. This technology offers the possibility of significant benefits to social welfare—saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises.
| Author | : Hao Dong |
| Publisher | : Springer Nature |
| Total Pages | : 526 |
| Release | : 2020-06-29 |
| Genre | : Computers |
| ISBN | : 9811540950 |
Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.
| Author | : Nicolae Tudoroiu |
| Publisher | : BoD – Books on Demand |
| Total Pages | : 172 |
| Release | : 2023-12-13 |
| Genre | : Transportation |
| ISBN | : 1837691770 |
Clean and efficient transportation in countries around the world is only possible if governments and scientists focus on stimulating and supporting the electric vehicle industry by developing and deploying the most advanced Li-ion battery technologies. Recently, several improvements have been made in the direction of operational safety, the elimination of explosion hazards, and the mitigation of chemical toxicity. The state of charge of an electric vehicle battery is an essential internal parameter that plays a vital role in utilizing the battery’s energy efficiency, operating safely in various realistic conditions and environments, and extending the battery’s life. Also, automated systems are integrated into the architecture of electrical vehicles, allowing for technology, machinery, or systems to perform tasks or processes with minimal human intervention. Automation in electric vehicles involves the integration of advanced technologies to enhance the driving experience, improve safety, optimize energy efficiency, and facilitate the transition to sustainable transportation. The key aspects of automation in electric vehicles are advanced driver assistance, self-driving capabilities, battery and energy management, and safety and collision avoidance. This book provides a comprehensive overview of electric and hybrid electric vehicles, exploring their design, the modeling of Li-ion battery management systems, state-of-charge estimation algorithms, and the most used electric motors. It also discusses new trends in electric vehicle automation as well as different control strategies.
| Author | : Lino Guzzella |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 345 |
| Release | : 2007-09-21 |
| Genre | : Technology & Engineering |
| ISBN | : 3540746927 |
The authors of this text have written a comprehensive introduction to the modeling and optimization problems encountered when designing new propulsion systems for passenger cars. It is intended for persons interested in the analysis and optimization of vehicle propulsion systems. Its focus is on the control-oriented mathematical description of the physical processes and on the model-based optimization of the system structure and of the supervisory control algorithms.
| Author | : National Research Council |
| Publisher | : National Academies Press |
| Total Pages | : 395 |
| Release | : 2013-04-14 |
| Genre | : Science |
| ISBN | : 0309268524 |
For a century, almost all light-duty vehicles (LDVs) have been powered by internal combustion engines operating on petroleum fuels. Energy security concerns about petroleum imports and the effect of greenhouse gas (GHG) emissions on global climate are driving interest in alternatives. Transitions to Alternative Vehicles and Fuels assesses the potential for reducing petroleum consumption and GHG emissions by 80 percent across the U.S. LDV fleet by 2050, relative to 2005. This report examines the current capability and estimated future performance and costs for each vehicle type and non-petroleum-based fuel technology as options that could significantly contribute to these goals. By analyzing scenarios that combine various fuel and vehicle pathways, the report also identifies barriers to implementation of these technologies and suggests policies to achieve the desired reductions. Several scenarios are promising, but strong, and effective policies such as research and development, subsidies, energy taxes, or regulations will be necessary to overcome barriers, such as cost and consumer choice.
| Author | : Antonio Sciarretta |
| Publisher | : Springer |
| Total Pages | : 306 |
| Release | : 2019-08-01 |
| Genre | : Technology & Engineering |
| ISBN | : 3030241270 |
This book elaborates the science and engineering basis for energy-efficient driving in conventional and autonomous cars. After covering the physics of energy-efficient motion in conventional, hybrid, and electric powertrains, the book chiefly focuses on the energy-saving potential of connected and automated vehicles. It reveals how being connected to other vehicles and the infrastructure enables the anticipation of upcoming driving-relevant factors, e.g. hills, curves, slow traffic, state of traffic signals, and movements of nearby vehicles. In turn, automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and to save energy. Lastly, the energy-efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles. Building on classical methods of powertrain modeling, optimization, and optimal control, the book further develops the theory of energy-efficient driving. In addition, it presents numerous theoretical and applied case studies that highlight the real-world implications of the theory developed. The book is chiefly intended for undergraduate and graduate engineering students and industry practitioners with a background in mechanical, electrical, or automotive engineering, computer science or robotics.
