Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles, Yeuching
Автор: Johannes Unger; Marcus Quasthoff; Stefan Jakubek Название: Energy Efficient Non-Road Hybrid Electric Vehicles ISBN: 3319297953 ISBN-13(EAN): 9783319297958 Издательство: Springer Рейтинг: Цена: 60940.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Название: Energy systems for electric and hybrid vehicles ISBN: 1785610082 ISBN-13(EAN): 9781785610080 Издательство: Неизвестно Рейтинг: Цена: 203850.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Electric and hybrid vehicles have been globally identified to be the most environmental friendly road transportation. Energy Systems for Electric and Hybrid Vehicles provides comprehensive coverage of the three main energy system technologies of these vehicles - energy sources, battery charging and vehicle-to-grid systems.
Автор: Kyriakos G. Vamvoudakis, Nick-Marios T. Kokolakis Название: Synchronous Reinforcement Learning-Based Control for Cognitive Autonomy ISBN: 1680837443 ISBN-13(EAN): 9781680837445 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 81310.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Describes the use of principles of reinforcement learning (RL) to design feedback policies for continuous-time dynamical systems that combine features of adaptive control and optimal control. The authors give an insightful introduction to reinforcement learning techniques that can address various control problems.
Автор: Das Shuvra Название: Modeling for Hybrid and Electric Vehicles Using Simscape ISBN: 1636391257 ISBN-13(EAN): 9781636391250 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 85010.00 T Наличие на складе: Нет в наличии. Описание:
Simscape, a Matlab/Simulink toolbox for modeling physical systems, is the ideal platform for developing and deploying models for hybrid and electric vehicle systems and sub-systems. This book is step-by-step guide through the process of developing precise and accurate models for all critical areas of hybrid and electric vehicles.
For electric and hybrid technology to deliver superior performance and efficiency, all sub-systems have to work seamlessly and in unison every time and all the time. To ensure this level of precision and reliability, modeling and simulation play crucial roles during the design and development cycle of electric and hybrid vehicles.
The majority of books currently on the market discuss relevant technologies and the physics and engineering of hybrid and electric vehicles. This book is unique by focusing on developing models of physical systems at the core of these vehicles using the tool of choice, Simscape. Relevant background and appropriate theory are referenced and summarized in the context of model development with significantly more emphasis on the model development procedure and obtaining usable and accurate results.
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.
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.
Автор: Vamvoudakis Kyriakos G., Wan Yan, Lewis Frank L. Название: Handbook of Reinforcement Learning and Control ISBN: 3030609898 ISBN-13(EAN): 9783030609894 Издательство: Springer Цена: 214280.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The Cognitive Dialogue: A New Architecture for Perception and Cognition.- Rooftop-Aware Emergency Landing Planning for Small Unmanned Aircraft Systems.- Quantum Reinforcement Learning in Changing Environment.- The Role of Thermodynamics in the Future Research Directions in Control and Learning.- Mixed Density Reinforcement Learning Methods for Approximate Dynamic Programming.- Analyzing and Mitigating Link-Flooding DoS Attacks Using Stackelberg Games and Adaptive Learning.- Learning and Decision Making for Complex Systems Subjected to Uncertainties: A Stochastic Distribution Control Approach.- Optimal Adaptive Control of Partially Unknown Linear Continuous-time Systems with Input and State Delay.- Gradient Methods Solve the Linear Quadratic Regulator Problem Exponentially Fast.- Architectures, Data Representations and Learning Algorithms: New Directions at the Confluence of Control and Learning.- Reinforcement Learning for Optimal Feedback Control and Multiplayer Games.- Fundamental Principles of Design for Reinforcement Learning Algorithms Course Titles.- Long-Term Impacts of Fair Machine Learning.- Learning-based Model Reduction for Partial Differential Equations with Applications to Thermo-Fluid Models' Identification, State Estimation, and Stabilization.- CESMA: Centralized Expert Supervises Multi-Agents, for Decentralization.- A Unified Framework for Reinforcement Learning and Sequential Decision Analytics.- Trading Utility and Uncertainty: Applying the Value of Information to Resolve the Exploration-Exploitation Dilemma in Reinforcement Learning.- Multi-Agent Reinforcement Learning: Recent Advances, Challenges, and Applications.- Reinforcement Learning Applications, An Industrial Perspective.- A Hybrid Dynamical Systems Perspective of Reinforcement Learning.- Bounded Rationality and Computability Issues in Learning, Perception, Decision-Making, and Games Panagiotis Tsiotras.- Mixed Modality Learning.- Computational Intelligence in Uncertainty Quantification for Learning Control and Games.- Reinforcement Learning Based Optimal Stabilization of Unknown Time Delay Systems Using State and Output Feedback.- Robust Autonomous Driving with Humans in the Loop.- Boundedly Rational Reinforcement Learning for Secure Control.
Автор: Vamvoudakis Название: Handbook of Reinforcement Learning and Control ISBN: 3030609928 ISBN-13(EAN): 9783030609924 Издательство: Springer Рейтинг: Цена: 214280.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including: * deep learning; * artificial intelligence; * applications of game theory; * mixed modality learning; and * multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.
Автор: Kohjiya Shinzo, Kato Atsushi, Ikeda Yuko Название: Reinforcement of Rubber: Visualization of Nanofiller and the Reinforcing Mechanism ISBN: 9811537917 ISBN-13(EAN): 9789811537912 Издательство: Springer Цена: 116150.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents the most recent description of rubber reinforcement, focusing on the network-like structure formation of nanofiller in the rubber matrix under the presence of bound rubber. In the case of natural rubber, the self-reinforcement effect is uniquely functioning, and new template crystallization is suggested.
Название: Reinforcement of Rubber ISBN: 9811537887 ISBN-13(EAN): 9789811537882 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents the most recent description of rubber reinforcement, focusing on the network-like structure formation of nanofiller in the rubber matrix under the presence of bound rubber. In the case of natural rubber, the self-reinforcement effect is uniquely functioning, and new template crystallization is suggested.
Автор: Denton, Tom Название: Electric and Hybrid Vehicles ISBN: 0367273233 ISBN-13(EAN): 9780367273231 Издательство: Taylor&Francis Рейтинг: Цена: 37760.00 T Наличие на складе: Нет в наличии. Описание: This straightforward and highly illustrated full colour textbook is endorsed by by the Institute of the Motor Industry, and introduces the subject for further education and undergraduate students as well as technicians. This new edition includes a new section on diagnostics and completely updated case studies.
Автор: Williamson, Sheldon S. Название: Energy management strategies for electric and plug-in hybrid electric vehicles ISBN: 1461477107 ISBN-13(EAN): 9781461477105 Издательство: Springer Рейтинг: Цена: 130610.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book addresses practical issues for commercialization of current and future electric and plug-in hybrid electric vehicles, covering power system architecture, battery technologies, battery management and interface with renewable energy, with examples.
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