SPECIAL SESSIONS

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Special Session 3



AI-based Design, Control, and Fault Diagnosis of Emerging

Electric Machines and Drives for Intelligent Power Applications


Organizers:

Rundong Huang, City University of Hong Kong

Zaixin Song, The Hong Kong Polytechnic University

Yang Xiao, University of Leicester


Brief Description:
Electric machines are the cornerstone of modern power systems, playing a critical role in applications ranging from renewable energy systems and electric vehicles to industrial automation and smart grids. With the rapid advancement of artificial intelligence (AI) technologies, there is a growing opportunity to revolutionize the design, control, and maintenance of electric machines, enabling higher efficiency, reliability, and simplified design. This special session aims to explore the latest developments and research in AI-driven solutions for electric machines, focusing on three key areas: AI-based design optimization, intelligent drive and control strategies, and fault diagnosis and predictive maintenance.

The session will cover the design and optimization of electric machines for enhanced performance and reduced energy consumption. It will also address advanced AI-based control algorithms for improving the dynamic response, efficiency, and adaptability of electric machine drives in complex power systems. The data-driven modelling and machine learning applied to electric machine design and control are focused. Furthermore, the session will highlight cutting-edge approaches for fault detection, diagnosis, and prognosis, leveraging AI to enable predictive maintenance and ensure the reliability of electric machines in critical applications.

By fostering interdisciplinary discussions and showcasing state-of-the-art research, this special session focuses on the design, drive, and fault diagnosis of electric machines for intelligent power applications. Topics of interest include but are not limited to:

• AI-driven design and optimization of electric machines
• Electric machines for renewable energy systems and industrial automation
• Data-driven modeling and digital twins for electric machines
• Drive topologies and control strategies for electric machines
• Fault-tolerant control and fault diagnosis
• Power converter/inverter for intelligent power applications

Keywords: Electric machines; Electric drives; Control strategies; Fault diagnosis; AI-based Algorithms
Submission Portal: https://easychair.org/my/conference?conf=peet2025

About the Organizers:

Dr. Rundong Huang received the B.Eng. degree in hydropower engineering, M.Eng. degrees in electrical engineering, and Ph.D. degree in electrical engineering from Huazhong University of Science and Technology, Wuhan, China, in 2017 and 2020, and the City University of Hong Kong in 2024, respectively. He is currently working as a postdoctoral fellow in the City University of Hong Kong, Hong Kong SAR, China.
His current research interests include the design, analysis, and optimization of axial-flux permanent-magnet machines, control and drive of electric machines, electric machines for propulsion systems and actuators, and analytical methods for electric machines.


Dr. Zaixin Song received the B.Eng. and M.Eng. degrees in electrical engineering and automation at Harbin Institute of Technology (HIT), Harbin, China, in 2016 and 2018, respectively. He received the Ph.D. degree majoring electrical engineering at City University of Hong Kong (CityU), Hong Kong, in 2021. In September 2021, he worked as a postdoctoral research fellow in CityU. In July 2022, he worked as a postdoctoral research fellow in Nanyang Technological University, Singapore. Currently, he is working as a Research Assistant Professor in State Key Laboratory of Ultra-precision Machining Technology (SKL-UMT), Department of Industrial and Systems Engineering at The Hong Kong Polytechnic University.
Dr. Song has been working on electric machinery for years. His current research interests include design and control of modern electric drives, smart manufacturing & robotics drives, sustainable electric propulsion machines, among other related fields. His expertise lies in the reliability design of electric machines and multiphysics modeling. Dr. Song has published over 90 research articles and authorised over 20 patents. He is currently Member of IEEE, Member of China Electrotechnical Society (CES), Member of IISE, Member of Elsevier Applied Energy Academy, Project Coordinator of SKL-UMT, and serves as a Session Chair of IEEE conferences for many times.


Dr. Yang Xiao received the Ph.D. degree in electronics and electrical engineering from University of Sheffield, UK, in 2021. Between 2021 to 2023, he was a senior research engineer in Dyson Technology Ltd., UK. In 2023, he was a Lecturer in School of Electrical and Electronics Engineering, Huazhong University of Science and Technology, China. In Dec. 2023, He joined School of Engineering, University of Leicester, UK, where he currently serves as a Lecturer in Electrical Machine and Deputy Director of Electrical and Electronics Engineering discipline. He also works as associate editor of IEEE Trans. Energy Conversion, financial chair of ICTEES 25, local chair of ISEEIE 24, and special session chairs in ECCE Euro 25 and ICEM 24. His current research interests include the design, control, and AI assisted optimization of electrical machines in electrified transportation systems, modern power systems, and advanced domestic and medical appliances.