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Elsevier

Artificial Intelligence and Data Science in Electric Vehicle Technology and Infrastructure

  • 1 Edición - 17 de noviembre de 2026
  • Última edición
  • Editores: V. Subramaniyaswamy, R. Bala Krishnan, R. Elakkiya, N. Rajesh Kumar
  • Idioma: Inglés

Artificial Intelligence and Data Science in Electric Vehicle Technology and Infrastructure offers a comprehensive exploration of how AI and data science are revolutionizing the el… Leer más

Descripción

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.

Puntos claves

  • Demonstrates how AI algorithms improve battery management, energy use, and vehicle performance to tackle EV reliability and efficiency issues
  • Explains how predictive analytics leverage data science and machine learning to prevent vehicle malfunctions, minimizing downtime and reducing maintenance costs
  • Showcases the development of smart charging infrastructure that utilizes data analysis to optimize energy distribution and significantly cut charging times
  • Discusses the role of AI and data science in advancing autonomous driving capabilities, enhancing safety and operational efficiency in transportation
  • Highlights innovative, data-driven solutions for sustainable energy, aiding in reducing carbon emissions and promoting environmentally friendly EV technologies

De interès para

Researchers and academicians in computer science, electrical engineering, artificial intelligence, data science, and machine learning; Industry professionals, such as software engineers and data scientists, working to develop and launch products using data science, AI, and ML-related to electric vehicles (EV), EV technologies, and infrastructure

Índice

  1. The Artificial Intelligence Powered Electric Vehicle Ecosystem: A Comprehensive Guide
  2. Foundations of Electric Vehicle Technology: An Overview for the Artificial Intelligence and Data Science Practitioner
  3. Intelligent Electric Mobility: The Role of Artificial Intelligence and Data in Shaping Sustainable Transportation
  4. Artificial Intelligence Driven Approaches for Optimal Electric Vehicle Charging Infrastructure
  5. Next-Generation Electric Mobility through Artificial Intelligence and Data Science
  6. Artificial Intelligence and Data Science Perspectives in Smart and Sustainable E-Mobility
  7. Selective Opposition-Based Grey Wolf Optimization for Intelligent Electric Vehicle Charging and Grid Integration
  8. Edge Artificial Intelligence in Electric Vehicles: Enabling Real-Time Artificial Intelligence and Low-Latency Analytics
  9. Cognitive and Data-Driven Intelligence in Electric Vehicle Systems: From System Foundations, Functionalities to Real-World Case Studies
  10. Blockchain enhanced network congestion management system in Electric vehicle charging stations
  11. Policy driven Multi-Population Memetic framework for electric vehicle charging station placement
  12. Energy-Aware Route Optimization for Electric Vehicles in real-time traffic conditions
  13. Harnessing Artificial Intelligence and Data for the Future of Electric Mobility
  14. A Review of Artificial Intelligence-Based Energy Management Strategies and Control Architectures in Electric Vehicle and Renewable Energy Integrated Sustainable Microgrids
  15. Harnessing LLMs and Intelligent Techniques for the Future of Electric Mobility
  16. Secured Framework for Electric Vehicle Charging Using SIMON Light-Weight Encryption
  17. Smart Contracts and Artificial Intelligence - Driven Data Analytics for Trustworthy Electric Vehicle Charging Infrastructure
  18. Privacy-Preserving Data Analytics in Electric Vehicle Ecosystems: Challenges and Artificial Intelligence - Driven Solutions
  19. Leveraging Artificial Intelligence for Data-Driven Business Models and Services in the Electric Vehicle Industry: Enhancing Customer Insights and Innovation
  20. Unlocking the potential of Artificial Intelligence and Data in the Electric Vehicle Revolution
  21. A Deep dive into Artificial Intelligence and Data Science for Electric Vehicle
  22. Artificial Intelligence based Ensemble Learning Framework for Air Quality Index Forecasting and Its Impact on Electric Vehicle Technology and Infrastructure
  23. Data-driven innovations in electric vehicle technology and applications
  24. Applications of Artificial Intelligence and Data Analytics in Electric Vehicle Systems
  25. Leveraging Artificial Intelligence for Personalized Driver Assistance and Energy Efficiency in a Connected Electric Vehicle Platform: A Case Study
  26. Leveraging Explainable Artificial Intelligence (XAI) and Machine Learning for Electric Vehicle Battery Lifespan Enhancement

Detalles del producto

  • Edición: 1
  • Última edición
  • Publicado: 17 de noviembre de 2026
  • Idioma: Inglés

Sobre los editores

VS

V. Subramaniyaswamy

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.

Afiliaciones y experiencia
Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

RK

R. Bala Krishnan

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).

Afiliaciones y experiencia
Associate Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

RE

R. Elakkiya

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.

Afiliaciones y experiencia
Associate Professor, Department of Computer Science, Birla Institute of Technology and Science, Pilani, Dubai Campus, Dubai International Academic City, Dubai, United Arab Emirates

NK

N. Rajesh Kumar

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.

Afiliaciones y experiencia
Assistant Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India