Contributed papers are solicited describing original works in AI-enhanced power electronics and sustainable energy systems. Topics and technical areas of interest include but are not limited to the following:

 

Track 1: AI and Machine Learning Foundations for Power Electronics and Energy Systems

• Machine learning, deep learning, and reinforcement learning for power electronics

• Physics-informed and data-driven modeling of power converters

• AI-based control and optimization for power electronic systems

• Explainable AI, trustworthy AI, and edge AI for energy applications

• Digital twins and AI-based simulation for power electronics

• Optimization algorithms and metaheuristics for energy systems

• Big data analytics and IoT for power electronics condition monitoring

• AI for fault diagnosis, prognosis, and predictive maintenance

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Track 2: AI-Enhanced Power Electronics Converters, Drives, and Control

• AI-driven design and control of DC-DC, DC-AC, and AC-DC converters

• Intelligent control of motor drives and electric machines

• Wide-bandgap device (SiC, GaN) applications with AI-based control

• Model predictive control and adaptive control enhanced by AI

• AI for power quality improvement and harmonic mitigation

• Neural network-based observer and estimator design for power electronics

• AI-assisted thermal management and reliability assessment

• Real-time implementation of AI algorithms on embedded platforms (FPGA, DSP, MCU)

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Track 3: Sustainable Energy Generation and Grid Integration

• AI for solar PV, wind, hydro, and hybrid renewable energy systems

• Forecasting of renewable energy generation using machine learning

• AI-based maximum power point tracking (MPPT) and power optimization

• Grid integration of renewables: stability, control, and protection

• AI for power system stability, reliability, and security assessment

• Intelligent inverters and grid-forming converters

• AI-enabled demand response and load management

• Smart grid, microgrid, and virtual power plant (VPP) operation with AI

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Track 4: AI for Energy Storage, Microgrids, and Smart Energy Management

• AI for battery management systems (BMS) and state estimation (SOC, SOH)

• Machine learning for energy storage scheduling and optimization

• AI-based control of supercapacitors, hydrogen, and hybrid storage systems

• Intelligent energy management systems (EMS) for buildings and industry

• AI for microgrid energy trading, peer-to-peer energy sharing, and blockchain

• Reinforcement learning for energy market participation and bidding

• AI for electric vehicle (EV) charging infrastructure and V2G

• Cybersecurity and privacy-preserving AI for smart energy systems

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Track 5: Applications, Digitalization, and Emerging Trends

• AI-enhanced power electronics for industrial automation and transportation

• AI for smart cities, smart buildings, and sustainable infrastructure

• Digital twins, edge computing, and cloud platforms for energy systems

• AI for carbon neutrality, life cycle assessment, and circular economy

• Green AI and energy-efficient AI hardware for power applications

• AI for offshore wind, hydrogen economy, and emerging energy technologies

• Human–AI collaboration in energy system operation and decision-making

• Standards, policies, and regulatory frameworks for AI in energy