Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Speed Control Of Hybrid Electric Vehicle Using Artificial Intelligence Techniques International Journal Of Computing And Network Technology 2014 Vol 2 No 1 PDF full book. Access full book title Speed Control Of Hybrid Electric Vehicle Using Artificial Intelligence Techniques International Journal Of Computing And Network Technology 2014 Vol 2 No 1.
| Author | : Japjeet Kaur |
| Publisher | : |
| Total Pages | : 7 |
| Release | : 2014 |
| Genre | : |
| ISBN | : |
| Author | : Jili Tao |
| Publisher | : Elsevier |
| Total Pages | : 348 |
| Release | : 2024-06-07 |
| Genre | : Science |
| ISBN | : 0443131902 |
Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management presents the state-of-the-art in hybrid electric vehicle system modelling and management. With a focus on learning-based energy management strategies, the book provides detailed methods, mathematical models, and strategies designed to optimize the energy management of the energy supply module of a hybrid vehicle.The book first addresses the underlying problems in Hybrid Electric Vehicle (HEV) modeling, and then introduces several artificial intelligence-based energy management strategies of HEV systems, including those based on fuzzy control with driving pattern recognition, multi objective optimization, fuzzy Q-learning and Deep Deterministic Policy Gradient (DDPG) algorithms. To help readers apply these management strategies, the book also introduces State of Charge and State of Health prediction methods and real time driving pattern recognition. For each application, the detailed experimental process, program code, experimental results, and algorithm performance evaluation are provided.Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management is a valuable reference for anyone involved in the modelling and management of hybrid electric vehicles, and will be of interest to graduate students, researchers, and professionals working on HEVs in the fields of energy, electrical, and automotive engineering. Provides a guide to the modeling and simulation methods of hybrid electric vehicle energy systems, including fuel cell systems Describes the fundamental concepts and theory behind CNN, MPC, fuzzy control, multi objective optimization, fuzzy Q-learning and DDPG Explains how to use energy management methods such as parameter estimation, Q-learning, and pattern recognition, including battery State of Health and State of Charge prediction, and vehicle operating conditions
| Author | : Chitra A. |
| Publisher | : John Wiley & Sons |
| Total Pages | : 288 |
| Release | : 2020-07-10 |
| Genre | : Computers |
| ISBN | : 1119682010 |
Electric vehicles/hybrid electric vehicles (EV/HEV) commercialization is still a challenge in industries in terms of performance and cost. The performance along with cost reduction are two tradeoffs which need to be researched to arrive at an optimal solution. This book focuses on the convergence of various technologies involved in EV/HEV. The book brings together the research that is being carried out in the field of EV/HEV whose leading role is by optimization techniques with artificial intelligence (AI). Other featured research includes green drive schemes which involve the possible renewable energy sources integration to develop eco-friendly green vehicles, as well as Internet of Things (IoT)-based techniques for EV/HEVs. Electric vehicle research involves multi-disciplinary expertise from electrical, electronics, mechanical engineering and computer science. Consequently, this book serves as a point of convergence wherein all these domains are addressed and merged and will serve as a potential resource for industrialists and researchers working in the domain of electric vehicles.
| Author | : Ajay Kumar |
| Publisher | : CRC Press |
| Total Pages | : 239 |
| Release | : 2024-08-14 |
| Genre | : Technology & Engineering |
| ISBN | : 1040095658 |
This book presents an analysis of the existing surveys in the literature of batteries, chargers, control systems, battery management systems, plugs, sockets, drives, and fuel-cell-based and plug-in smart electric and hybrid vehicles. It provides detailed comparative analysis of parameters of intelligent electric and hybrid vehicles with conventional vehicles and in-depth knowledge of power electronics tools in smart electric and hybrid vehicles. This book compiles the research work and findings in advancements of smart electric and hybrid vehicles from automobile, mechanical, electronic, electrical, computer science, and allied engineering domains and explains how smart electric and hybrid vehicles can be utilized without harmful emissions over the entire lifecycle of a vehicle and how reliance on fossil fuels can be reduced. Features: Discusses different types of lightweight and robust materials for the fabrication of smart electric and hybrid vehicles Presents computational models, emerging technologies, numerical techniques, and environmental and economic benefits applicable to electric vehicles Highlights the applications of smart and hybrid vehicles in diverse sectors including aerospace, agriculture, defense, and medical Covers electromechanical drives, storage systems, wireless charging, and component design for smart electric and hybrid vehicles Showcases a detailed comparative analysis of parameters of intelligent electric and hybrid vehicles with conventional vehicles It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electrical and electronics engineering, computer engineering, and automotive engineering.
| Author | : Teng Liu |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 99 |
| Release | : 2019-09-03 |
| Genre | : Technology & Engineering |
| ISBN | : 1681736195 |
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 | : Ajay Kumar |
| Publisher | : CRC Press |
| Total Pages | : 213 |
| Release | : 2024-08-14 |
| Genre | : Technology & Engineering |
| ISBN | : 1040099440 |
In this book, recent developments, the future outlook, and advanced and analytical modeling techniques of smart electric and hybrid vehicles are explained with examples backed by experimental and numerical data. It also discusses the integration of newer developments like digital twin, artificial intelligence, nature-inspired algorithms, Internet of Things, and the role of Industry 4.0 in advancements in vehicle engineering. It compiles overall aspects of advancements in smart electric and hybrid vehicles by bringing the latest research and development by comprehensive range of mathematical, numerical, and simulation modeling, and management techniques to strengthen the engineering science and technological developments for the future. Features: • This book focuses on contemporary aspects of smart electric and hybrid vehicles techniques for new means and models for green environment. • Discusses the role of artificial intelligence, machine learning, and machine vision tools in smart electric and hybrid vehicles. • Presents design and analysis of charging stations and their sustainability roadmap for smart electric vehicles. • Highlights the cyber and functional security of intelligent and hybrid vehicles. • Explains diagnostics, prognostics, reliability, and durability issues in smart electric and hybrid vehicles. • Covers the Internet of Things-based battery and charging management approach and effect of voltage drop in charging capacity of smart electric vehicles. It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and automotive engineering.
| Author | : Emanuele Crisostomi |
| Publisher | : CRC Press |
| Total Pages | : 261 |
| Release | : 2017-10-30 |
| Genre | : Technology & Engineering |
| ISBN | : 1498745008 |
This book explores the behavior of networks of electric and hybrid vehicles. The topics that are covered include: energy management issues for aggregates of plug-in vehicles; the design of sharing systems to support electro-mobility; context awareness in the operation of electric and hybrid vehicles, and the role that this plays in a Smart City context; and tools to test and design massively large-scale networks of such vehicles. The book also introduces new and interesting control problems that are becoming prevalent in the EV-PHEV's context, as well as identifying some open questions. A particular focus of the book is on the opportunities afforded by networked actuation possibilities in electric and hybrid vehicles, and the role that such actuation may play in air-quality and emissions management.
| Author | : Amir Taghavipour |
| Publisher | : Springer |
| Total Pages | : 198 |
| 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 | : Li Yeuching |
| Publisher | : Springer Nature |
| Total Pages | : 123 |
| Release | : 2022-06-01 |
| Genre | : Technology & Engineering |
| ISBN | : 3031792068 |
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.
| Author | : Yangsheng Xu |
| Publisher | : McGraw Hill Professional |
| Total Pages | : 302 |
| Release | : 2013-09-22 |
| Genre | : Technology & Engineering |
| ISBN | : 0071826823 |
Build state-of-the-art intelligent omnidirectional HEVs Engineer high-performance, low-emission automobiles by overcoming traditional obstacles and efficiently harnessing energy from multiple sources. Hybrid Electric Vehicle Design and Control features complete coverage of all electrical, mechanical, and software components. Find out how to develop fast-charging battery systems, efficiently manage power, implement independent steering and force control, and enhance driving stability and controllability. This comprehensive guide offers detailed modeling, testing, and tuning techniques and provides an overview of emerging developments in hybrid technologies. Coverage includes: 4WIS and 4WID hardware and software Hybrid vehicle design structures Zero-radius turning and lateral parking Steer-by-wire and extended steering Behavior-based and zero-radius steering Traction force distribution and stability Battery, energy, and power management systems Cell equalization and fast-charging control MPC, load forecasting, and neural network classifi cation Best performance techniques
