Sustainable Composites for Automotive Engineering presents recent trends in this important research field. Emphasis is placed on the development, characterization, and application of lightweight composites in various automobile components. The types of materials covered include polymer composites, metal matrix and ceramic matrix composites. The book takes a 360-degree approach and covers all aspects of the product development cycle including materials selection, as well as design and development processes, testing, characterization, modelling and simulation, and applications. The book will be a valuable reference resource for academic and industrial researchers, materials scientists and engineers, industrial R&D, automotive engineers, and manufacturers working in the design and development of composite materials for applications in automotive components.
Autonomous Electric Vehicles explores cutting-edge technologies revolutionizing transportation and city navigation. Novel solutions to the control problem of the complex nonlinear dynamics of robotized electric vehicles are developed and tested. The new control methods are free of shortcomings met in control schemes which are based on diffeomorphisms and global linearization (complicated changes of state variables, forward and backwards state-space transformations, singularities). It is shown that such methods can be used in the steering and traction system of several types of robotized electric vehicles without needing to transform the state-space model of these systems into equivalent linearized forms. It is also shown that the new control methods can be implemented in a computationally simple manner and are also followed by global stability proofs.
Human-Machine Systems Design and Evaluation Methodology for Intelligent Vehicles examines the fields of designing and developing intelligent design and intelligent vehicle driving evaluation by using virtual reality, augmented reality, and other technologies. The book explains the methodologies and systems of interactive design, user evaluation and testing using virtual reality technology and augmented reality technology in intelligent cockpit design. With the rising prominence of electric vehicles and automatic driving (assisted) technology, intelligent vehicles are becoming a reality.Compared to traditional interactive design, artificial intelligence provides new opportunities and challenges for the interactive design of intelligent cockpit space, especially under the condition of intelligent assisted driving, the driver's behavior performance, multimodal interactive display interface design and evaluation.
Vehicular Platoon System Design: Fundamentals and Robustness provides a comprehensive introduction to connected and automated vehicular platoon system design. Platoons decrease the distances between cars or trucks using electronic, and possibly mechanical, coupling. This capability allows many cars or trucks to accelerate or brake simultaneously. It also allows for a closer headway between vehicles by eliminating reacting distance needed for human reaction. The book considers the key issues of robustness and cybersecurity, with optimization-based model predictive control schemes applied to control vehicle platoon.In the controller design part, several practical problems, such as constraint handling, optimal control performance, robustness against disturbance, and resilience against cyberattacks are reviewed. In addition, the book provides detailed theoretical analysis of the stability of the platoon under different control schemes.
Terramechanics and Off-Road Vehicle Engineering: Terrain Behaviour and Off-Road Mobility, Third Edition provides comprehensive coverage of terrain behavior, mechanics of wheel- and track-terrain interaction, and various types of models for cross-country performance, ranging from empirical, through theoretical, to physics-based engineering models. The physics-based models for wheeled and tracked vehicle performance developed under the direction of Prof. J.Y. Wong have been gaining increasingly wider acceptance by industry and governmental agencies. The mathematical models established for vehicle-terrain systems enable the engineering practitioner to evaluate a wide range of options and select an appropriate vehicle configuration for any given mission and environment.This long-anticipated revision presents the fields’ significant developments over the past decade, both through updates to existing chapters and the inclusion of new material related to modeling applications in addition to a notable, state-of-the-art excursus on extra-terrestrial rovers.
Construction Methods for an Autonomous Driving Map in an Intelligent Network Environment not only supports the development of Intelligent & Connected Transportation, but also promotes the landing application of autonomous driving. Areas covered include the fusion target perception method based on vehicle vision and millimeter wave radar, cross-field of view object perception method, vehicle motion recognition method based on vehicle road fusion information, vehicle trajectory prediction method based on improved hybrid neural network and driving map construction driven by road perception fusion are introduced in this book.Benefiting from the development of computer technique, the advanced machine learning and artificial intelligence theories are used by this book to show readers the construction process of the Autonomous Driving Map.
Machine Learning in Manufacturing: Quality 4.0 and the Zero Defects Vision reviews process monitoring based on machine learning algorithms and the technologies of the fourth industrial revolution and proposes Learning Quality Control (LQC), the evolution of Statistical Quality Control (SQC). This book identifies 10 big data issues in manufacturing and addresses them using an ad-hoc, 5-step problem-solving strategy that increases the likelihood of successfully deploying this Quality 4.0 initiative. With two case studies using structured and unstructured data, this book explains how to successfully deploy AI in manufacturing and how to move quality standards forward by developing virtually defect-free processes. This book enables engineers to identify Quality 4.0 applications and manufacturing companies to successfully implement Quality 4.0 practices.
Advances in Lithium-Ion Batteries for Electric Vehicles: Degradation Mechanism, Health Estimation, and Lifetime Prediction examines the electrochemical nature of lithium-ion batteries, including battery degradation mechanisms and how to manage the battery state of health (SOH) to meet the demand for sustainable development of electric vehicles. With extensive case studies, methods and applications, the book provides practical, step-by-step guidance on battery tests, degradation mechanisms, and modeling and management strategies. The book begins with an overview of Li-ion battery aging and battery aging tests before discussing battery degradation mechanisms and methods for analysis.Further methods are then presented for battery state of health estimation and battery lifetime prediction, providing a range of case studies and techniques. The book concludes with a thorough examination of lifetime management strategies for electric vehicles, making it an essential resource for students, researchers, and engineers needing a range of approaches to tackle battery degradation in electric vehicles.
Human-Machine Interaction for Automated Vehicles: Driver Status Monitoring and the Takeover Process explains how to design an intelligent human-machine interface by characterizing driver behavior before and during the takeover process. Multiple solutions are presented to accommodate different sensing technologies, driving environments and driving styles. Depending on the availability and location of the camera, the recognition of driving and non-driving tasks can be based on eye gaze, head movement, hand gesture or a combination. Technical solutions to recognize drivers various behaviors in adaptive automated driving are described with associated implications to the driving quality. Finally, cutting-edge insights to improve the human-machine-interface design for safety and driving efficiency are also provided, based on the use of this sensing capability to measure drivers’ cognition capability.
Decision-Making Techniques for Autonomous Vehicles provides a general overview of control and decision-making tools that could be used in autonomous vehicles. Motion prediction and planning tools are presented, along with the use of machine learning and adaptability to improve performance of algorithms in real scenarios. The book then examines how driver monitoring and behavior analysis are used produce comprehensive and predictable reactions in automated vehicles. The book ultimately covers regulatory and ethical issues to consider for implementing correct and robust decision-making. This book is for researchers as well as Masters and PhD students working with autonomous vehicles and decision algorithms.