Intelligent Energy Management In Hybrid Electric Vehicles PDF Download
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| Author | : Teng Liu |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 99 |
| Release | : 2019-09-03 |
| Genre | : Technology & Engineering |
| ISBN | : 1681736195 |
Download Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles Book in PDF, ePub and Kindle
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application. In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.
| Author | : Hamid Khayyam |
| Publisher | : |
| Total Pages | : |
| Release | : 2010 |
| Genre | : Technology |
| ISBN | : |
Download Intelligent Energy Management in Hybrid Electric Vehicles Book in PDF, ePub and Kindle
Intelligent Energy Management in Hybrid Electric Vehicles.
| Author | : Simona Onori |
| Publisher | : Springer |
| Total Pages | : 121 |
| Release | : 2015-12-16 |
| Genre | : Technology & Engineering |
| ISBN | : 1447167813 |
Download Hybrid Electric Vehicles Book in PDF, ePub and Kindle
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 | : Rashad Mustafa |
| Publisher | : |
| Total Pages | : 151 |
| Release | : 2014 |
| Genre | : |
| ISBN | : 9783844033403 |
Download Intelligent Energy Management for a Parallel Hybrid Electric Vehicle in Customer Use Book in PDF, ePub and Kindle
| Author | : Teng Liu |
| Publisher | : Synthesis Lectures on Advances |
| Total Pages | : 99 |
| Release | : 2019-09-03 |
| Genre | : Computers |
| ISBN | : 9781681736204 |
Download Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles Book in PDF, ePub and Kindle
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application. In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.
| Author | : Chitra A. |
| Publisher | : John Wiley & Sons |
| Total Pages | : 288 |
| Release | : 2020-07-21 |
| Genre | : Computers |
| ISBN | : 1119681901 |
Download Artificial Intelligent Techniques for Electric and Hybrid Electric Vehicles Book in PDF, ePub and Kindle
Electric vehicles are changing transportation dramatically and this unique book merges the many disciplines that contribute research to make EV possible, so the reader is informed about all the underlying science and technologies driving the change. An emission-free mobility system is the only way to save the world from the greenhouse effect and other ecological issues. This belief has led to a tremendous growth in the demand for electric vehicles (EV) and hybrid electric vehicles (HEV), which are predicted to have a promising future based on the goals fixed by the European Commission's Horizon 2020 program. This book brings together the research that has been carried out in the EV/HEV sector and the leading role of advanced optimization techniques with artificial intelligence (AI). This is achieved by compiling the findings of various studies in the electrical, electronics, computer, and mechanical domains for the EV/HEV system. In addition to acting as a hub for information on these research findings, the book also addresses the challenges in the EV/HEV sector and provides proven solutions that involve the most promising AI techniques. Since the commercialization of EVs/HEVs still remains a challenge in industries in terms of performance and cost, these are the two tradeoffs which need to be researched in order to arrive at an optimal solution. Therefore, this book focuses on the convergence of various technologies involved in EVs/HEVs. Since all countries will gradually shift from conventional internal combustion (IC) engine-based vehicles to EVs/HEVs in the near future, it also serves as a useful reliable resource for multidisciplinary researchers and industry teams.
| Author | : |
| Publisher | : |
| Total Pages | : |
| Release | : 2004 |
| Genre | : |
| ISBN | : |
Download An Intelligent Energy Management System for Charging of Plug-in Hybrid Electric Vehicles at a Municipal Parking Deck Book in PDF, ePub and Kindle
There is a need to address potential problems due to the emergence of technologies that will affect the utility industry in a time horizon of less than 20 years. One such technology is the plug-in hybrid electric vehicle (PHEV); the emergence of these vehicles in the marketplace poses a potential threat to the existing power grid. With a large number of these vehicles ÃØâ'ƠËœplugged-inÃØâ'Ơâ"Ø for charging, in the absence of control over the power drawn, the additional load can result in grid instabilities and disruptions. As a solution to alleviate such a situation and to allow for smooth integration of PHEVs into the grid, an ÃØâ'ƠÅ"intelligent energy management systemÃØâ'ƠÂ (iEMS) is proposed in this thesis. The iEMS intelligently allocates power to the vehicle battery chargers through real time monitoring and control, to ensure optimal usage of available power, charging time and grid stability. The research presented here provides the conceptualization of the system architecture and the definition of its components, their attributes and interactions. A Simulink based simulator incorporating the dynamics of the real world scenario at a municipal parking deck with random plug-in/out times and varying initial states of charge is presented. A mathematical framework is provided for developing the iEMS algorithm for the optimal power allocation strategy under utility power constraints; taking into consideration the vehicle battery parameters and user preferences. The formulation and solution of the optimization is also proposed for a chosen objective function followed by the presentation of simulation results. The thesis concludes with the description of an experimental setup consisting of a Labview based GUI along with ZigBee communication nodes which is a first step towards validating the system performance in a real-world deployment.
| Author | : Maude Josée Blondin |
| Publisher | : Springer Nature |
| Total Pages | : 434 |
| Release | : 2022-05-28 |
| Genre | : Science |
| ISBN | : 3030844749 |
Download Intelligent Control and Smart Energy Management Book in PDF, ePub and Kindle
This volume aims to provide a state-of-the-art and the latest advancements in the field of intelligent control and smart energy management. Techniques, combined with technological advances, have enabled the deployment of new operating systems in many engineering applications, especially in the domain of transport and renewable resources. The control and energy management of transportation and renewable resources are shifting towards autonomous reasoning, learning, planning and operating. As a result, these techniques, also referred to as autonomous control and energy management, will become practically ubiquitous soon. The discussions include methods, based on neural control (and others) as well as distributed and intelligent optimization. While the theoretical concepts are detailed and explained, the techniques presented are tailored to transport and renewable resources applications, such as smart grids and automated vehicles. The reader will grasp the most important theoretical concepts as well as to fathom the challenges and needs related to timely practical applications. Additional content includes research perspectives and future direction as well as insight into the devising of techniques that will meet tomorrow’s scientific needs. This contributed volume is for researchers, graduate students, engineers and practitioners in the domains of control, energy, and transportation.
| Author | : Preetika Kulshrestha |
| Publisher | : |
| Total Pages | : 81 |
| Release | : 2009 |
| Genre | : |
| ISBN | : |
Download An Intelligent Energy Management System for Charging of Plug-in Hybrid Electric Vehicles at a Municipal Parking Deck Book in PDF, ePub and Kindle
Keywords: energy management system, optimization, PHEV, smart grid, control.
| Author | : Li Yeuching |
| Publisher | : Springer Nature |
| Total Pages | : 123 |
| Release | : 2022-06-01 |
| Genre | : Technology & Engineering |
| ISBN | : 3031792068 |
Download Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles Book in PDF, ePub and Kindle
The urgent need for vehicle electrification and improvement in fuel efficiency has gained increasing attention worldwide. Regarding this concern, the solution of hybrid vehicle systems has proven its value from academic research and industry applications, where energy management plays a key role in taking full advantage of hybrid electric vehicles (HEVs). There are many well-established energy management approaches, ranging from rules-based strategies to optimization-based methods, that can provide diverse options to achieve higher fuel economy performance. However, the research scope for energy management is still expanding with the development of intelligent transportation systems and the improvement in onboard sensing and computing resources. Owing to the boom in machine learning, especially deep learning and deep reinforcement learning (DRL), research on learning-based energy management strategies (EMSs) is gradually gaining more momentum. They have shown great promise in not only being capable of dealing with big data, but also in generalizing previously learned rules to new scenarios without complex manually tunning. Focusing on learning-based energy management with DRL as the core, this book begins with an introduction to the background of DRL in HEV energy management. The strengths and limitations of typical DRL-based EMSs are identified according to the types of state space and action space in energy management. Accordingly, value-based, policy gradient-based, and hybrid action space-oriented energy management methods via DRL are discussed, respectively. Finally, a general online integration scheme for DRL-based EMS is described to bridge the gap between strategy learning in the simulator and strategy deployment on the vehicle controller.
