Artificial Intelligence and Data Science in Electric Vehicle Technology and Infrastructure

Erscheinungsjahr 2026
ISBN: 978-0-443-44326-8
Verlag: Elsevier Science

Buch, Englisch, 400 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 449 g

Artificial Intelligence and Data Science in Electric Vehicle Technology and Infrastructure offers a comprehensive exploration of how AI and data science are revolutionizing the electric vehicle (EV) industry. It guides readers through the basic concepts of EV technology and explains how machine learning and blockchain optimize battery management, predictive maintenance, and secure fault detection. The book highlights cutting-edge techniques like sensor fusion and computer vision for autonomous driving, alongside real-time analytics and edge computing for low-latency AI applications. It also covers intelligent charging infrastructure, route optimization, and renewable energy integration and shares insights into cybersecurity, business models, and demand forecasting, complemented by practical case studies.

This book is a useful resource for researchers, scientists, advanced students, software engineers, data scientists, R&D professionals, and other industrial personnel working at the intersection of computer science, electrical engineering, artificial intelligence, data science, and machine learning with an interest in advancing AI and ML applications in electric vehicle technologies.

Autoren/Hrsg.

Subramaniyaswamy, V.

Krishnan, R. Bala

Elakkiya, R.

Kumar, N. Rajesh

Fachgebiete

• Technische Wissenschaften Technik Allgemein Technik: Allgemeines
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Verkehrstechnik | Transportgewerbe Fahrzeugtechnik

Inhaltsverzeichnis

1. Foundations of Electric Vehicle Technology: An Overview for the AI/Data Science Practitioner
2. The Role of Data Acquisition and Management in Electric Vehicle Ecosystems: Sensors, IoT, and Cloud Integration
3. Machine Learning and Blockchain-Enabled Optimization of Battery Management Systems for Accurate SoC, SoH, and Thermal Monitoring in EVs
4. Predictive Maintenance for Electric Vehicle Powertrains: Leveraging Sensor Data, V2G and AI and Block Chain for Secure Fault Detection and Prognostics
5. Computer Vision and Sensor Fusion for Autonomous Electric Vehicles: Perception, Localization, and Decision-Making
6. Edge Computing and Real-time Analytics in Electric Vehicles: Enabling Low-Latency AI Applications
7. Intelligent Charging Infrastructure: AI-Powered Optimization of Charging Schedules, Load Balancing, and Grid Integration
8. AI and Data Science Applications for Electric Vehicle Ecosystems and Beyond: Route Optimization and Renewable Energy Integration and its Efficiency in Electric Vehicles: AI-Driven Navigation and Range Prediction
9. Behavior Analysis and Personalization in Electric Vehicles: Insights from Telematics and AI for Enhanced Safety and Security
10. Natural Language Processing for Enhanced Human-Machine Interaction in Electric Vehicles: Voice Assistants and In-Car Infotainment
11. Cybersecurity in Connected Electric Vehicles and Ultra-Fast Charging Infrastructure: AI-Driven Threat Detection and Mitigation
12. Data-Driven Business Models and Services in the Electric Vehicle Industry: Utilizing AI for Customer Insights and Innovation
13. Predictive Analytics for Electric Vehicle Sales, Demand Forecasting, and Infrastructure Planning: Leveraging Time Series Analysis and Machine Learning
14. Case Study: Data-Driven Battery Analytics for Enhanced Lifespan and Performance
15. Case Study: Leveraging AI for Personalized Driver Assistance and Energy Efficiency in a Connected EV Platform

Über Autor(innen)

Elakkiya, R.
Dr. R. Elakkiya is an Assistant Professor in the Department of Computer Science, Birla Institute of Technology & Science, Pilani, Dubai Campus. She received her PhD from Anna University, Chennai, in 2018. She secured the University First Rank and was awarded the Gold Medal during master’s in software engineering from CEG Campus, Anna University, Chennai. She won the iDEX - DISC 4 challenge and received the grant award from DIO, DRDO in 2021 and Young Achiever Award from INSc in 2019. She had received many extra-mural funded projects from various government and non-government agencies and served as Machine Learning and Data Analytics Consultant and delivered many products to different industry verticals. She is Member of the Association of Computing Machinery and Lifetime Member of International Association of Engineers.

Krishnan, R. Bala
Dr R. Bala Krishnan is currently working as Assistant Professor in the Department of Computer Science and Engineering, Srinivasa Ramanujan Centre, SASTRA Deemed to be University, Kumbakonam. In total, he has more than 15 years of experience in academia and research. He received his MTech and PhD in Computer Science from SASTRA Deemed University in 2012 and 2021 respectively. His current research interests include quantum computing, machine learning, artificial intelligence, intrusion detection and prevention systems, information hiding, image processing and cryptography. He is a Lifetime Member in Indian Society of Technical Education (ID:85432) and International Association of Engineers (ID:327906).

Kumar, N. Rajesh
Dr. N. Rajesh Kumar is working as Assistant Professor in the Department of Computer Science and Engineering, Srinivasa Ramanujan Centre, SASTRA Deemed University, Kumbakonam. He received his master’s degree in computer applications from Alagappa University, Karaikudi in 2009 and a PhD degree in Computer Science from SASTRA Deemed University in 2021. His research interests include information hiding, image processing, and visual cryptography. He has published several research articles in journals and conferences of repute. He is Lifetime Member of various technical societies, such as ISTE and IAENG.

Subramaniyaswamy, V.
Dr V. Subramaniyaswamy is currently working as a Professor in the School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India. In total, he has 18 years of experience in academia. He has published papers in reputed international journals and conferences and filed multiple patents. His technical competencies lie in recommender systems, Artificial Intelligence, the Internet of Things, reinforcement learning, big data analytics, and cognitive analytics. He has edited Electric Motor Drives and their Applications, with Simulation Practice (Elsevier: 2022, ISBN: 9780323911627), among other books.