Artificial Intelligence-Empowered Modern Electric Vehicles in Smart Grid Systems

Fundamentals, Technologies, and Solutions

1st Edition - May 23, 2024
Editors: Aparna Kumari, Sudeep Tanwar
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 3 8 1 4 - 7
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 3 8 1 5 - 4

Artificial Intelligence-Empowered Modern Electric Vehicles in Smart Grid Systems: Fundamentals, Technologies, and Solutions is an essential reference for energy researche… Read more

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Artificial Intelligence-Empowered Modern Electric Vehicles in Smart Grid Systems: Fundamentals, Technologies, and Solutions is an essential reference for energy researchers, graduate students, and engineers who aim to understand the opportunities offered by artificial intelligence for the integration of electric vehicles into smart grids. The book covers foundational knowledge, including the essentials of artificial intelligence and its applications for electric vehicles in a clear and holistic manner. Next, it breaks down two essential areas of application in more detail: energy management (from to energy harvesting to demand response and complex forecasting), and market strategies (including peer-to-peer, vehicle-to-vehicle, and vehicle-to-everything trading, plus cybersecurity implications).

A final part provides detailed case studies and close consideration of challenges, including code and data sets for replication of techniques. Providing a clear pathway from fundamentals to practical implementation, this book provides multidisciplinary guidance for implementing this cutting-edge technology in the energy systems of the future.

Cover image
Title page
Table of Contents
Copyright
Dedication
List of contributors
Preface
Acknowledgments
Part I: Introduction to artificial intelligence and electric vehicles in smart grid systems
Chapter 1. Introduction to artificial intelligence-empowered electric vehicles in smart grids
Abstract
Chapter Outline
1.1 Introduction
1.2 Background
1.3 Artificial intelligence techniques
1.4 Applications
1.5 Challenges
1.6 Case study
1.7 Case study: Analysis of artificial intelligence–driven optimization in managing electric vehicle charging within a smart grid
1.8 Results
1.9 Conclusion
References
Chapter 2. Communication technologies for electric vehicles
Abstract
Chapter Outline
2.1 Introduction
2.2 Literature review
2.3 Artificial intelligence in electric vehicles
2.4 Communication needs at customer premises
2.5 Smart grid communication technologies
2.6 Comparison of various approaches/applications for communication technologies for electric vehicles
2.7 Application area of artificial intelligence-empowered electric vehicles
2.8 Case study for integration of communication technologies for electric vehicles
2.9 Conclusion
References
Chapter 3. Robustness and resilience of artificial intelligence-enabled electric vehicles: a safety and security perspective
Abstract
Chapter Outline
3.1 Introduction
3.2 Literature review
3.3 Threat analysis and risk assessment
3.4 Robustness and resilience framework
3.5 Ethical and legal considerations
3.6 Regulatory frameworks and compliance
3.7 Privacy protection in artificial intelligence-enabled electric vehicles
3.8 Ethical guidelines for safety and security in artificial intelligence-enabled electric ehicles
3.9 Conclusion and future scope
References
Chapter 4. Applications of AI-empowered electric vehicles for voice recognition in Asian and Austronesian languages
Abstract
Chapter Outline
4.1 Introduction to Internet of Things and smart grid systems
4.2 Voice-controlled Internet of Things in electric vehicles
4.3 Speech recognition systems and multilanguages translation
4.4 Issues in voice-controlled Internet of Things for Asian and Austronesian languages
4.5 Computational aspects of voice-based Internet of Things in Asian and Austronesian language translation
4.6 Case study on the implementations of artificial intelligence-enabled Deep Neural Network language models supporting electric vehicles and autonomous vehicles
4.7 Conclusion
References
Part II: Energy management for artificial intelligence-empowered modern electric vehicles
Chapter 5. Demand response management using artificial intelligence-empowered electric vehicles
Abstract
Chapter Outline
5.1 Introduction
5.2 Demand response in electric vehicles—survey
5.3 Applied constraints for electric vehicles
5.4 Growth of battery management systems
5.5 Computer vision
5.6 Artificial intelligence-based predictive algorithms
5.7 Deep reinforcement learning
5.8 Fault analysis in battery systems
5.9 Artificial intelligence-empowered filtering algorithms
5.10 Current approaches and opportunities
5.11 Conclusion
References
Chapter 6. Energy prediction for future energy supply in electrical vehicles
Abstract
Chapter Outline
6.1 Introduction
6.2 Literature review
6.3 System model and problem formulation
6.4 Result and discussion
6.5 Conclusion and future work
References
Chapter 7. Optimizing the integration of renewable energy source and plug-in electric vehicles for economic emission load dispatch through soft computing techniques
Abstract
Chapter Outline
7.1 Introduction
7.2 Literature survey
7.3 System model and problem formulation
7.4 Sine cosine algorithm for optimizing economic emission load dispatch with renewable energy source and plug-in electric vehicles
7.5 Results and discussion
7.6 Conclusion
References
Chapter 8. Battery-operated electric vehicles and plug-in hybrid electric vehicles
Abstract
Chapter Outline
8.1 Introduction
8.2 Related work
8.3 Proposed model
8.4 Experimental result
8.5 Conclusion
References
Part III: Energy trading for artificial intelligence-empowered modern electric vehicles
Chapter 9. Peer-to-peer energy trading using renewable energy sources and electric vehicles
Abstract
Chapter Outline
9.1 Introduction
9.2 Peer-to-peer energy trading basic
9.3 Advantages of peer-to-peer energy trading
9.4 Integration of renewable energy sources
9.5 Role of electric vehicles in energy trading
9.6 Benefits of peer-to-peer energy trading with renewables and electric vehicles
9.7 AI Empowerment in peer-to-peer energy trading
9.8 Challenges and future outlook
9.9 Regulatory and policy challenges
9.10 Conclusion
References
Chapter 10. Smart energy management using vehicle-to-vehicle and vehicle-to-everything
Abstract
Chapter Outline
10.1 Introduction
10.2 Literature review
10.3 Fundamentals of vehicle-to-vehicle communication
10.4 Vehicle-to-everything communication
10.5 Smart energy management in transportation
10.6 Enabling technologies for smart energy management
10.7 Case studies and real-world implementations
10.8 Future trends and challenges
10.9 Conclusion
References
Chapter 11. Vehicle-to-vehicle power transfer
Abstract
Chapter Outline
11.1 Introduction
11.2 Related works
11.3 Methods and materials
11.4 Working principles of wireless charging
11.5 Conclusion
References
Chapter 12. Cyber-security challenges for artificial intelligence-empowered electric vehicles—analysis and current status
Abstract
Chapter Outline
12.1 Introduction
12.2 Related work
12.3 Detailed classification of security issues
12.4 Non-technology based issues
12.5 Different types of security attacks in electric vehicles
12.6 Other security challenges ignored by electric vehicle companies
12.7 What happens when an electric vehicle gets hacked
12.8 IT security regulations used in electric vehicle
12.9 Assessment of security
12.10 Case study
12.11 Future trend
12.12 Conclusion
References
Part IV: Case studies and futuristic trends for artificial intelligence-enabled electric vehicles
Chapter 13. Revitalizing traffic control: the synergy of fog computing and Internet of Things for an intelligent traffic light system for electric vehicles
Abstract
Chapter Outline
13.1 Introduction
13.2 Literature review
13.3 Methodology
13.4 Analysis
13.5 Revitalizing traffic control: the synergy of fog computing and Internet of Things for an intelligent traffic light system
13.6 Conclusion
References
Chapter 14. On fuzzy completely irresolute on smart decision-making for electric vehicle systems
Abstract
Chapter Outline
14.1 Introduction to fuzzy irresolute of artificial intelligence and electric vehicles in smart grid system
14.2 Literature survey
14.3 Proposed approach
14.4 Fuzzy weakly completely γ-irresolute mapping via artificial intelligence-empowered electric vehicles
14.5 Conclusion
References
Chapter 15. Front-in parking method for intelligent electric vehicles using proportional–integral–derivative control
Abstract
Chapter Outline
15.1 Introduction
15.2 Organization
15.3 Related work
15.4 Establishment of vehicle kinematic model
15.5 Path planning algorithm based on geometric deduction
15.6 Determination of extreme positions
15.7 Forward stage
15.8 Movement to the garage stage
15.9 Storage stage
15.10 Reverse stage
15.11 Simulation detail and result analysis
15.12 Cosimulation with Simulink and CarSim
15.13 CarSim parameter settings
15.14 Path planning and tracking controller
15.15 Drive the car into the garage and gradually align the vehicle parallel to the garage
15.16 Conclusion
References
Further reading
Chapter 16. The futuristic trends for modern electric vehicles
Abstract
Chapter Outline
16.1 Introduction
16.2 Artificial intelligence in electric vehicle wireless power transfer systems in smart grids
16.3 Artificial intelligence in energy management
16.4 The role of artificial intelligence on the future of electric vehicles
16.5 Advanced technologies in electric vehicle
16.6 Future of artificial intelligence in electric vehicle industry
16.7 Discussion
16.8 Conclusion
References
Chapter 17. Environmental sustainability and carbon footprint reduction through artificial intelligence-enabled energy management in electric vehicles
Abstract
Chapter Outline
17.1 Introduction
17.2 Literature review
17.3 Need and benefits of artificial intelligent-enabled energy management for electrical vehicles
17.4 Conclusion
17.5 Future scope
References
Index

Aparna Kumari

Dr. Aparna Kumari is an Assistant Professor in the Dept. of Computer Science and Engineering at the Institute of Technology, Nirma University, India. She was listed among the top 2% most influential researchers globally by Stanford University in 2021, 2022, and 2023 and was also awarded the Shri Pralhad P Chhabria Award – Runner Up for Best Female Professional (Early Career) in 2023. Her research interests include smart grid technologies, blockchain, Artificial Intelligence, quantum computing, electric vehicles, virtual and augmented reality, cybersecurity, and IoT. She is currently serving as a reviewer for the IEEE Internet of Things Journal, Multimedia Tools and Applications (Springer), Sensors (MDPI), the Journal of Information Security and Applications (Elsevier), and IEEE Network. She is a member of the Program/Technical Committee on UPCON2020, IC4S-2021, IC4S-2022, and EPTL2022, as well as of IEEE Women in Engineering.

Affiliations and expertise

Assistant Professor, Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

Sudeep Tanwar

Sudeep Tanwar is a full Professor at Nirma University, India, where he also leads the ST Research Laboratory. He is a Visiting Professor with Jan Wyzykowski University, Poland and the University of Pitesti, Romania. He has edited 20 books and authored 4 books and more than 300 technical articles, for national and international peer-reviewed journals and conferences. He initiated the research field of blockchain technology adoption in various verticals in 2017. His research interests include blockchain technology, wireless sensor networks, fog computing, smart grid, and the IoT. He is a member of the Technical Committee on Tactile Internet of IEEE Communication Society. He has been awarded Best Research Paper Awards from IEEE IWCMC-2021, IEEE ICCCA-2021, IEEE GLOBECOM 2018, IEEE ICC 2019, and Springer ICRIC-2019. He has won Dr KW Wong Annual Best Paper Prize (with 750 USD) for 2021 sponsored by Elsevier (publishers of JISA).

Affiliations and expertise

Professor, Computer Science and Engineering Department, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health