AI-Driven Power Electronics and Intelligent Energy Systems for Sustainable Mobility and Renewable Integration aims to explore the transformative role of Artificial Intelligence (AI) in the design, control, and optimization of modern power electronics systems, focusing on applications in sustainable mobility and renewable energy integration. The session emphasizes the utilization of AI techniques including machine learning, deep learning, reinforcement learning, and predictive analytics to enhance the efficiency, reliability, and adaptability of power electronic converters, energy storage systems, and smart grids. One major area of interest is AI-enhanced power converter design and control, where intelligent algorithms enable real-time optimization of voltage, current, and power factor in grid-tied and standalone systems, while also providing adaptive control for Wide Bandgap (WBG) semiconductor-based converters, which are increasingly important for high-efficiency and high-density power applications. Another focus area is intelligent energy management in renewable systems, where AI-driven forecasting and optimization enable more accurate prediction of renewable generation and load demand, as well as efficient scheduling of energy storage systems (ESS). These capabilities facilitate dynamic power allocation between renewable sources, storage units, and loads, enhancing grid stability and ensuring efficient utilization of available energy resources. The session also addresses sustainable mobility applications, particularly the integration of AI in electric vehicle (EV) systems. Topics include intelligent EV charging and discharging strategies, vehicle-to-grid (V2G) integration, and AI-assisted traction drive management to maximize energy efficiency and extend battery life, while supporting grid stability. In addition, the session highlights the importance of digital twin and cyber-physical modeling for power electronics and energy systems. AI-enabled digital twins provide real-time monitoring, predictive maintenance, and fault diagnosis capabilities, enabling the early detection of system anomalies and reducing downtime. Such models also facilitate the simulation and optimization of complex hybrid energy systems, integrating distributed generation, storage, and loads. Moreover, the session explores AI applications in smart grids and decentralized energy systems, including predictive control, adaptive protection, and real-time optimization of distributed energy resources. AI techniques can enhance grid stability, power quality, and resiliency, particularly when integrating variable renewable sources in microgrid and islanded operations. Emerging trends such as AI-based thermal management of power devices, data-driven reliability optimization, and the convergence of AI, IoT, and power electronics for sustainable urban energy systems are also discussed, reflecting the interdisciplinary nature of modern energy challenges. Overall, this special session seeks to bring together researchers, practitioners, and industry experts working on AI-driven innovations in power electronics and energy systems, providing a platform for sharing cutting-edge research, practical implementations, and visionary concepts. By addressing the convergence of AI, renewable energy, and electric mobility, the session aims to inspire the development of intelligent, adaptive, and sustainable energy solutions, fostering collaboration between academia and industry and shaping the future of power electronics and smart energy systems. Participants will gain insights into the latest methodologies, tools, and applications, as well as opportunities to explore collaborative research and technology transfer for sustainable energy solutions
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