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Abstract Submission Deadline:26/04/2026
Part I: Foundations and Emerging Technologies
Chapter 1: Overview of Next-Generation Energy Storage Technologies
1.1 Introduction
1.2 Limitations of Conventional Energy Storage Systems 1.3 Emerging Energy Storage Technologies
1.4 Hybrid and Advanced Storage Concepts 1.5 Key Performance Metrics and Evaluation 1.6 Industrial Applications and Case Studies
1.7 Challenges, Research Directions, and Future Trends 1.8 Summary and Outlook
References
Chapter 2: Role of Artificial Intelligence in Modern Energy Ecosystems
2.1 Introduction
2.2 AI Techniques for Energy Forecasting 2.3 Optimization of Energy Storage Systems 2.4 AI in Grid Control and Decision-Making 2.5 Case Studies and Applications
2.6 Challenges and Limitations
2.7 Future Directions
2.8 Summary
References
Chapter 3: Next-Generation Batteries Technologies
3.1 Introduction
3.2 Lithium-Ion Batteries
3.3 Solid-State Batteries
3.4 Sodium-Ion and Lithium-Sulfur Batteries
3.5 Flow Batteries
3.6 Comparative Performance Analysis
3.7 Applications and Deployment
3.8 Challenges and Research Directions
3.9 Summary References
Part II: AI-Enabled Smart Energy Storage Systems
Chapter 4: AI-Enhanced Energy Storage-Enabled Energy Systems
4.1 Introduction
4.2 AI-Based Energy Optimization
4.3 Integration with Smart Grids
4.4 AI for Renewable Energy and EV Charging
4.5 Case Studies and Applications
4.6 Challenges and Limitations
4.7 Future Trends
4.8 Summary
References
Chapter 5: AI in Energy Storage Markets, Policy, and Regulation
5.1 Introduction
5.2 AI in Market Operation and Pricing
5.3 Regulatory Frameworks and Policy Design
5.4 Economic Optimization of Energy Storage Systems
5.5 Carbon Markets and Energy Equity
5.6 Challenges and Opportunities
5.7 Future Directions
5.8 Summary
References
Chapter 6: AI-Empowered Green Hydrogen Energy Storage Systems
6.1 Introduction
6.2 Hydrogen Production and Storage Technologies
6.3 AI for System Modeling and Optimization
6.4 Predictive Control and Performance Evaluation
6.5 Integration with Renewable Energy
6.6 Case Studies
6.7 Challenges and Research Directions
6.8 Summary
References
Part III: Integration, Digitalization, and Security
Chapter 7: Artificial Intelligence Techniques for Smart Grids and Electric Vehicles
7.1 Introduction
7.2 AI for Smart Grid Energy Management
7.3 AI in Microgrids
7.4 Electric Vehicle Integration and V2G Strategies
7.5 Load Forecasting and Energy Optimization
7.6 Case Studies 7.7 Challenges and Future Trends
7.8 Summary
References
Chapter 8: Blockchain for Energy Storage Systems
8.1 Introduction
8.2 Blockchain Fundamentals for Energy Systems
8.3 Decentralized Energy Trading
8.4 Smart Contracts for Storage Management
8.5 Data Security and Integrity
8.6 Case Studies and Industrial Applications
8.7 Challenges and Research Directions
8.8 Summary
References
Chapter 9: Big Data, IoT, and Digital Twins for Battery Monitoring Systems
9.1 Introduction
9.2 IoT-Enabled Sensors and Data Acquisition
9.3 Cloud Analytics and Machine Learning
9.4 Digital Twin Modeling for Batteries
9.5 Predictive Diagnostics and Optimization
9.6 Case Studies and Applications
9.7 Challenges and Future Directions
9.8 Summary
References
Chapter 10: Cybersecurity for Energy Storage Systems
10.1 Introduction
10.2 Threats to Connected Energy Storage Systems
10.3 Risk Mitigation Strategies
10.4 Cybersecurity Frameworks
10.5 AI and Security Integration
10.6 Case Studies
10.7 Challenges and Future Directions
10.8 Summary
References
Chapter 11: Future Directions and Challenges in Smart Energy Storage Systems
11.1 Introduction
11.2 Emerging Storage Technologies
11.3 Integration with AI, Hydrogen, and Blockchain
11.4 Circular Economy Strategies
11.5 Global Deployment Scenarios
11.6 Roadmap to 2050
11.7 Challenges and Research Opportunities
11.8 Summary
References
* Any other chapter fitting within the scope of the books are welcome. Authors are requested to mention the title of their chapter of interest as per the above mentioned table of contents.
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