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Elsevier

Books in Automotive engineering

The Automotive Engineering portfolio spans vehicle design, powertrain technologies, emissions control, autonomous systems, and manufacturing processes. It delivers state-of-the-art research and practical applications for engineers and researchers driving innovation in mobility, electric vehicles, and smart transportation. Covering sustainability, AI, and safety, this collection equips professionals with knowledge to tackle industry challenges and develop next-generation automotive solutions.

  • Terramechanics and Off-Road Vehicle Engineering

    Terrain Behaviour and Off-Road Mobility
    • 3rd Edition
    • J.Y. Wong
    • English
    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

    • 1st Edition
    • Zhijun Chen
    • English
    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
    • 1st Edition
    • Carlos A. Escobar + 1 more
    • English
    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
    • 1st Edition
    • Haifeng Dai + 1 more
    • English
    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
    • 1st Edition
    • Yifan Zhao + 2 more
    • English
    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-interf... 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

    • 1st Edition
    • Jorge Villagra + 1 more
    • English
    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.

  • Theories and Practices of Self-Driving Vehicles

    • 1st Edition
    • Qingguo Zhou + 4 more
    • English
    Self-driving vehicles are a rapidly growing area of research and expertise. Theories and Practice of Self-Driving Vehicles presents a comprehensive introduction to the technology of self driving vehicles across the three domains of perception, planning and control. The title systematically introduces vehicle systems from principles to practice, including basic knowledge of ROS programming, machine and deep learning, as well as basic modules such as environmental perception and sensor fusion. The book introduces advanced control algorithms as well as important areas of new research. This title offers engineers, technicians and students an accessible handbook to the entire stack of technology in a self-driving vehicle. Theories and Practice of Self-Driving Vehicles presents an introduction to self-driving vehicle technology from principles to practice. Ten chapters cover the full stack of driverless technology for a self-driving vehicle. Written by two authors experienced in both industry and research, this book offers an accessible and systematic introduction to self-driving vehicle technology.

  • Braking of Road Vehicles

    • 2nd Edition
    • Andrew J. Day + 1 more
    • English
    Braking of Road Vehicles, Second Edition includes updated and new subject matter related to the technological advances of road vehicles such as hybrid and electric vehicles and "self-driving" and autonomous vehicles. New material to this edition includes root causes, guidelines, experimental and measurement techniques, brake NVH identification and data analysis, CAE and dynamic modelling, advances in rotor and stator materials, manufacturing methods, changes to European and US legislation since 2014, recent developments in technology, methods and analysis, and new and updated case studies. This new edition will continue to be of interest to engineers and technologists in automotive and road transport industries, automotive engineering students and instructors, and professional staff in vehicle-related legislational, legal, military, security and investigative functions.

  • Autonomous and Connected Heavy Vehicle Technology

    • 1st Edition
    • Rajalakshmi Krishnamurthi + 2 more
    • English
    Autonomous and Connected Heavy Vehicle Technology presents the fundamentals, definitions, technologies, standards and future developments of autonomous and connected heavy vehicles. This book provides insights into various issues pertaining to heavy vehicle technology and helps users develop solutions towards autonomous, connected, cognitive solutions through the convergence of Big Data, IoT, cloud computing and cognition analysis. Various physical, cyber-physical and computational key points related to connected vehicles are covered, along with concepts such as edge computing, dynamic resource optimization, engineering process, methodology and future directions. The book also contains a wide range of case studies that help to identify research problems and an analysis of the issues and synthesis solutions. This essential resource for graduate-level students from different engineering disciplines such as automotive and mechanical engineering, computer science, data science and business analytics combines both basic concepts and advanced level content from technical experts.

  • Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines

    • 1st Edition
    • Jihad Badra + 3 more
    • English
    Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines summarizes recent developments in Artificial Intelligence (AI)/Machine Learning (ML) and data driven optimization and calibration techniques for internal combustion engines. The book covers AI/ML and data driven methods to optimize fuel formulations and engine combustion systems, predict cycle to cycle variations, and optimize after-treatment systems and experimental engine calibration. It contains all the details of the latest optimization techniques along with their application to ICE, making it ideal for automotive engineers, mechanical engineers, OEMs and R&D centers involved in engine design.