Tutorial I

                                                                                                                                                        Yunhong Che

                                                                                                                            MIT & Aalborg University

Bio: Dr Yunhong Che is a Research Fellow in Chemical Engineering at MIT and an Assistant Professor of Energy at Aalborg University (joint appointment). His work sits at the intersection of AI and electrochemistry, developing physics-informed models and diagnostics for batteries and energy systems. He earned his PhD from Aalborg University (2024) and previously visited EPFL and Stanford in 2023.

Title: Beyond Black Boxes: Physics-Guided AI for Battery Health in Electric Transportation

Abstract: Accurate and reliable state monitoring, health diagnosis, and lifetime prediction are critical to ensure the safe operation of batteries in energy storage systems. Factors such as different battery types, varying battery pack topologies, diverse user scenarios, and regional characteristics contribute to significant pattern differences, thus challenging optimal management. Integrating artificial intelligence technologies has brought new opportunities for the intelligent management of batteries. However, existing battery system management still faces challenges such as low model generalizability, poor generalization capability, and weak mechanistic interpretability. This seminar will introduce three main topics to address the above challenges. The first part of this seminar focuses on the development of algorithms for dynamic state estimation and prediction throughout the entire lifecycle of battery systems. An integrated multi-state estimation and prediction framework applicable to battery systems under varying operating conditions across their full lifecycle will be illustrated. Then, focusing on battery health prediction research tailored to practical applications with limited labeled data and model adaptability requirements, transfer learning-based model enhancement strategies suitable for variable data conditions will be introduced. Finally, targeting the development of interpretable models that integrate mechanism-based and data-driven approaches, as well as online non-destructive health diagnostics for batteries, a multi-source information fusion and mechanism-data-coupled interpretable battery health diagnosis and prognosis technology will be presented.

Tutorial II

                                                                                                                                                Xiao Chen

                                                                                                                                 University of Sheffield

Bio: Dr. Xiao Chen is a senior lecturer in electrical machines at University of Sheffield. He led various research projects / work packages on bearing currents, high frequency effects and manufacturing effects in electrical machines, high-fidelity modelling of electrical machines, and high-speed machines, funded by EPSRC, ORE CATAPULT, Royal Society, ATI, and Rolls-Royce.

Title: Bearing currents in electrical machines

Abstract: Bearing problems contribute to at least 20% of electrical machine failures, and this figure goes even higher for large machines (e.g. machines in wind turbines, more electric aircraft, etc). This tutorial will first introduce the mechanism of bearing currents in electrical machines driven by voltage source inverters, followed by a literature review of bearing current modelling approaches and mitigation techniques. Then, the bearing current research activities at University of Sheffield will be presented, including bearing impedance modelling, parasitic capacitance modelling, common mode, stator and rotor impedance modelling, combined electrical discharge machining and circulating bearing current modelling and validation, and zig-zag slot opening technique for bearing current mitigation.

Tutorial III

                                                                                                                         Muhammad Zubair 

                                                                                                                                 University of Glasgow

Bio: Dr. Muhammad Zubair (SFHEA, SMIEEE, SMOptica) received his PhD in Electronics and Communication Engineering from Politecnico di Torino, Italy, followed by postdoctoral research at the SUTD-MIT International Design Centre, Singapore. Before joining Leicester as an Assistant Professor (UK Lecturer), he held academic and research appointments at the James Watt School of Engineering, University of Glasgow; King Abdullah University of Science and Technology (KAUST); Information Technology University (ITU), Lahore; and the Singapore University of Technology and Design (SUTD). Dr. Zubair’s research interests span applied electromagnetics and metasurfaces, with a focus on developing next-generation models, materials, and devices for future communication, sensing, imaging, and energy applications. He has contributed as PI/ Co-PI or researcher co-lead in projects funded by several international agencies, including the EPSRC (UK), Qatar National Research Fund (QNRF), HEC/PHEC (Pak), Singapore Temasek Labs, and the US Department of Defense (DoD). He has published over 200 peer-reviewed articles, co-authored two book chapters/monographs, and has been listed among the top 2% most-cited researchers worldwide by Stanford-Elsevier since 2022. His contributions have been recognized with several awards, including the URSI Young Scientist Award, Punjab Innovation Research Challenge Award, IEEE Education (ETOP 2025) Change Champion, and the RSC Emerging Investigator 2024/2025 distinction. He is a Senior Fellow of the Higher Education Academy, Senior Member IEEE, Senior Member Optica, IEEE AP-S Young Professionals Ambassador 2025, Queen Elizabeth Prize for Engineering (QEPrize) Ambassador 2025, and an active member of IET, ACES, and SPIE. He currently serves or has served as an Associate Editor for IEEE Access, PLOS ONE, Wiley International Journal of Antennas and Propagation, and IET Microwaves, Antennas & Propagation.

Title: Metasurfaces for Next-Gen Communication and Sustainable Energy Innovations

Abstract: This tutorial explores the transformative role of metamaterials and metasurfaces as versatile platforms for next-generation electromagnetic (EM) wave manipulation across a wide range of applications. Metasurfaces have the potential to revolutionize communication, sensing, imaging, and sustainable energy by enabling unprecedented control over EM waves in ultra-thin and highly customizable structures. The presentation will cover advanced metasurface applications, such as reconfigurable intelligent surfaces for 5G/6G communication, high-sensitivity sensors for health and transportation, high-resolution imaging systems, and energy-efficient absorbers for solar and thermophotovoltaic devices. By examining recent breakthroughs and future directions, this talk aims to inspire innovative solutions and collaborations within the wider engineering community.