This e-book details state-of-the-art drive by wire technology. Readers are made aware about the challenges ahead that need be addressed in order for this technology to gain a foothold in the automotive industry. The eBook presents a systematic analysis of these issues and points out the fundamental reasons for the limited applications of these systems to date. It also discusses how recent technological advances in fault tolerant control can address these current challenges facing drive by wire application in automobiles. Non-technological issues such as consumer perception of drive by wire systems, trade-off analysis of drive by wire system from Automotive OEMs and suppliers’ perspective are also reviewed in this volume. This book will enhance current understanding of this technology amongst practicing automotive engineers and researchers alike.

Federated Learning Based Intelligent Systems to Handle Issues and Challenges in IoVs (Part 1) examines how federated learning can address key challenges within the Internet of Vehicles, from data security to routing efficiency. This volume explores how federated learning, a decentralized approach to machine learning, enables secure and adaptive IoV systems that enhance road safety, optimize traffic flow, and support reliable data sharing. Chapters cover essential topics, including technologies to address IoV routing issues, secure data exchange using blockchain, privacy-preserving methods, and NLP applications for vehicle safety. By combining theoretical insights with practical solutions, the book highlights how federated learning fosters scalable, resilient IoV systems that respond dynamically to the demands of connected vehicles. Key Features: - Addresses data privacy, secure communication, and adaptive solutions in IoV - Explores federated learning applications in real-time IoV systems - Combines practical examples with theoretical foundations in IoV technology - Includes emerging research areas in IoV federated learning frameworks

Federated Learning for Internet of Vehicles: IoV Image Processing, Vision, and Intelligent Systems (Volume 3) explores how federated learning is revolutionizing the Internet of Vehicles (IoV) by enabling secure, decentralized, and scalable solutions. Combining theoretical insights with practical applications, this book addresses key challenges such as data privacy, heterogeneous information, and network latency in IoV systems. This volume offers cutting-edge strategies to build intelligent, resilient vehicular systems, from privacy-enhanced data collection to blockchain-based payments, smart transportation systems, and vehicle number plate recognition. It highlights how federated learning drives advancements in secure data sharing, identity-based authentication, and real-time road safety improvements. Key Features: - In-depth exploration of federated learning applications in IoV. - Solutions for privacy, security, and scalability challenges. - Practical examples of blockchain integration and smart systems. - Insights into future research directions for IoV.

This book is a compilation of knowledge about computer models in the three-phase stator reference frame. Chapters explore several aspects of the topic and build upon research previously presented by contributors. The book aims to provide interesting solutions to problems encountered in the design of railway and analysis in railway motors. The modeling approaches proposed by the authors in this book may become an incentive for readers and researchers to develop their ‘lifehacks’ to solve new problems in induction motor design and testing. Key topics presented in the book: - Approximate calculations of induction motor equivalent T-shaped circuit parameters with the use of catalogue data - Simulations of different types of shaft load, including fluid coupling - Receiving static characteristics of an electric machine during simulation by means of dynamic model - Simulation of the electric drive’s specific applications with three-phase induction motors building - Direct start of an induction motor as part of an auxiliary drive of an AC electric locomotive, containing a capacitor phase splitter, starting with different types of shaft loads (fan or compressor). Special attention has been given to the description of the thermal model of an induction motor with a squirrel-cage rotor, which makes it possible to simulate operating modes when powered by an unbalanced voltage, as well as with squirrel cage defects. The thermal model is presented as a detailed superstructure to the model of electromechanical processes of an induction electric machine. Other key features of the book include references for further reading, an appendix for the parameters of the equivalent thermal circuit of an NVA-55 induction motor. The material presented in the book is of interest to railway motor engineers, specialists in electromechanics and electric drives who use SPICE-compatible CAD applications in their work.

Electric Vehicle Technologies: Trends, Control, and Charging Solutions explores the latest innovations transforming electric transportation. This concise and accessible volume presents advances in electric vehicle (EV) systems, focusing on solar energy integration, intelligent motor control, battery charging technologies, and IoT-based applications. The book is divided into three thematic sections. It begins with solar-powered charging systems and hardware design for sustainable mobility. It then covers power electronics and motor control methods, including BLDC control and adaptive charging frameworks. The final section addresses cutting-edge developments like vehicle-to-grid (V2G) systems and IoT-enabled EV automation. Key Features: - Advances in solar EV charging and wireless power transfer. - Control strategies for EV motors and battery charging. - Integration of IoT and autonomous systems in EVs. - Hardware designs for electric bicycles and solar charging stations. - V2G systems and adaptive power control.

Positioned at the intersection of intelligent transportation systems (ITS), computer vision, and machine learning, this book presents a comprehensive examination of how artificial intelligence and statistical techniques are reshaping traffic monitoring, management, and urban mobility in the era of smart cities. The book begins with the core principles of AI and traffic systems, introducing statistical modeling, data acquisition, and image processing for traffic analysis. Midway, it transitions into deep learning-powered applications such as object detection, vehicle tracking, congestion forecasting, and real-time incident recognition. Later sections address legal, regulatory, and ethical frameworks, while concluding chapters highlight IoT-enabled models and future trajectories in AI-powered traffic management. Key Features: Introduces principles of AI, machine learning, and statistical modeling for traffic systems Demonstrates applications of deep learning in congestion prediction, incident detection, and vehicle tracking Examines AI-driven traffic optimization, urban mobility solutions, and self-driving technologies Evaluates security, data privacy, and legal considerations in AI-based traffic surveillance Integrates AI with IoT frameworks for real-time monitoring in smart city infrastructure Highlights future directions and policy implications for sustainable and ethical traffic management.