AI-Driven Advances in Intelligent Power Electronic Systems Throughout Life-Cycle Phases

Dear Colleagues,

The intersection of artificial intelligence (AI) and power electronics is reshaping the fundamental architecture of our energy future. The flourishing development of AI technology drives high-quantity demand for electrical power, while simultaneously creating new opportunities for building intelligent power electronic systems across all life-cycle phases, including modeling, design, control, and maintenance. Distinguished from the conventional physics-based perspective, AI-driven approaches are establishing a novel paradigm based on the compression and comprehension of massive data information, enabling less reliance on expensive physics information, reducing human labor costs, and significantly enhancing performance in various research domains of power electronic systems, such as magnetic component modeling, predictive power control, and device lifetime estimation. However, AI technology is not a one-size-fits-all solution for all the challenges faced by power electronic systems. Issues such as data scarcity, computational resource limitations, lack of model interpretability, and cybersecurity concerns continue to hinder the application of existing AI technologies in industrial applications. Addressing these challenges requires innovative and tailored AI-driven advances to ensure their reliability, adaptability, and accuracy in intelligent power electronic systems.

This Special Issue aims to delve into the AI-driven advances in intelligent power electronic systems throughout their life-cycle phases: modeling, design, control, and maintenance. It invites researchers to contribute their insights and findings on a wide range of topics, including but not limited to:

• Efficient modeling methods of power components and systems based on AI;
• Digital twin and virtual prototyping analysis for power electronic systems;
• Automatic power converter design and optimization;
• Predictive and resilient power electronic control;
• Online learning for adaptive control in power electronic systems;
• Life-cycle device monitoring and system maintenance;
• Fault diagnostics and prediction of power electronic systems;
• Cyber–physical approaches for power electronic system hardening;
• Real-time hardware-in-loop (HIL) simulation of AI-driven methods;
• Optimized powering of AI architecture and platform development and design.

Dr. Hongjian Lin
Dr. Yangxiao Xiang
Dr. Yu Zeng
Dr. Yu Xiao
Dr. Xiangyu Meng
Guest Editors

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