AI Fusion in Energy Systems: Neural Networks and Bayesian Approaches for EVGI and EMS Optimization
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".
Deadline for manuscript submissions: 25 May 2026 | Viewed by 20
Special Issue Editors
Interests: complex and advance infrastructure; power electronic circuits; high voltage; numerical simulations; AI algorithm; artificial intelligence and robotics; next gen EV technology; electromagnetism; embedded systems; renewable energy generation telecommunication system; lightning protections
Special Issues, Collections and Topics in MDPI journals
Interests: hardware and software embedded projects; next gen EV technology; battery and advance charging system; VGI-V2X system; efficient embedded circuits; renewable energy generation; infrastructure; real time diagnosis; data mining; development of artificial intelligence and robotics; power electronic circuits; practical applied physics; high voltage; transdisciplinary science and engineering fields
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We invite original research contributions that explore the convergence of electric vehicle (EV) grid integration, artificial intelligence (AI) algorithms, innovative artificial neural network, Bayesian signal processing and advanced control systems within modern energy management frameworks. As EV penetration accelerates and renewable energy sources proliferate, coordinated control strategies are essential for maintaining grid stability and optimizing energy distribution. This Special Issue seeks innovative methodologies that leverage AI-driven control architecture for dynamic energy management in distributed generation systems. We particularly welcome studies applying Bayesian signal processing for uncertainty modelling and real-time decision-making in EV charging and discharging schedules. Emphasis will be placed on integrated solutions that harmonize renewable energy variability with vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations. Submissions addressing scalable control systems and predictive analytics that enhance the responsiveness of energy management systems are highly encouraged. Contributions may also explore the fusion of probabilistic inference techniques with AI models for enhanced forecasting and adaptive control. In addition, an important part of this Special Issue will be related to data encryption, data privacy and protection and blockchain systems related to the security of intelligent optimization systems in the above topics. Join us in shaping the next generation of sustainable, intelligent and resilient power systems.
Prof. Dr. Naoto Nagaoka
Dr. Minella Bezha
Prof. Dr. Bert de Vries
Guest Editors
Manuscript Submission Information
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Keywords
- AI fusion
- energy-system optimization
- neural networks
- Bayesian inference
- electric vehicle grid integration (EVGI)
- energy-management systems (EMS)
- Bayesian neural networks
- smart grid
- demand response
- deep learning in energy
- probabilistic modelling
- load forecasting
- reinforcement learning for EMS
- EV-charging optimization
- hybrid AI models
- renewable energy integration
- uncertainty quantification
- grid reliability
- real-time energy optimization
- data-driven energy system’s energy management optimization
- accurate SoH estimation
- new smart-battery management systems
- battery mix chemistry in a single EV pack
- drive-cycle creation
- micro-grid analysis based on a pool of EVs
- energy storage ageing and degradation
- vehicle-to-grid integration scenarios
- life-cycle assessment
- second-life energy storage applications
- AI tools and optimization in EV tech
- optimal battery lifetime during fast charging
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