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: 32142.00 р. 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: 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: 12197.00 р. 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: Vamvoudakis Название: Handbook of Reinforcement Learning and Control ISBN: 3030609928 ISBN-13(EAN): 9783030609924 Издательство: Springer Рейтинг: Цена: 32142.00 р. 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.
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: 13974.00 р. 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: Niroumand, Hamed Dr Название: Soil Reinforcement for Anchor Plates and Uplift Response ISBN: 012809558X ISBN-13(EAN): 9780128095584 Издательство: Elsevier Science Рейтинг: Цена: 15159.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Soil Reinforcement for Anchor Plates and Uplift Response presents a comprehensive and rigorous review of the current knowledge in soil improvement for anchor plates, and is based on original research that includes experimental data on how to enhance uplift response of soil anchor plates by using several soil reinforcement methods.
Divided into 6 chapters, the author makes an introduction to both Ancho Plates and Soil Reinforcement in chapter one, then providing a comprehensive literature review on the topic in chapter 2. Chapter 3 presents how the experiment was set up, the different types of geotextiles used, and the types of soil tested. Chapter 4 presents experimental data, along with data provided by simulation softwares, including Plaxis. Chapter 5 compares the experimental results to the numerical simulation data, providing researchers and geotechnical engineers with tools they can apply to their own projects.
In chapter 6, the author presents his conclusions and recommendations on the usage of soil reinforcement to maximize uplift response to anchor plates. Researchers in geotechnical engineering can use the methods and experimental data presented in the book on their own projects, and practicing engineers will benefit from the comparisons between experimental and simulation data provided to make appropriate selection of soil reinforcement techniques that can be applied to their projects.
Автор: Denton, Tom Название: Electric and Hybrid Vehicles ISBN: 0367273233 ISBN-13(EAN): 9780367273231 Издательство: Taylor&Francis Рейтинг: Цена: 5664.00 р. 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: Johannes Unger; Marcus Quasthoff; Stefan Jakubek Название: Energy Efficient Non-Road Hybrid Electric Vehicles ISBN: 3319297953 ISBN-13(EAN): 9783319297958 Издательство: Springer Рейтинг: Цена: 9141.00 р. 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.
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.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
