Artificial Intelligence and Data Science in Electric Vehicle Technology and Infrastructure
Will be published approx. on 3. November 2026
Book
Paperback/Softback
400 pages
978-0-443-44326-8 (ISBN)
Description
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.
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.
More details
Content
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
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