Biography:Professor Wei Xu is Director of the State Key Laboratory of High-Density Electromagnetic Energy and Power Systems in Institute of Electrical Engineering, Chinese Academy of Sciences. He has mainly focused on the developments of high-density electrical machines and drives. He has innovatively proposed the unequal amplitude vector theory and significantly improved the traction capacity and energy efficiency of electrical machines and systems. He has served as General Chairs for international conferences three times, and internationally Steering Committee Member for LDIA, and Associate Editor of IEEE TIE and IEEE TPEL. His academic works have been cited over 13,000 times by statistics of Google Scholar with H-index 58. He has been awarded as an IEEE Fellow, IET Fellow, IEEE IES Distinguished Lecturer, Elsevier China Highly Cited Scholar, etc.
Researchers and engineers from electrical, mechanical and information fields may find this lecture very useful when dealing with transportation motor and drive related design, control, system integration, which can be extended to other industrial applications.
King' s College London, UK
Biography:Yihua Hu , Professor and Ph.D. Supervisor, is an IET Fellow, a Royal Society Industry Fellow, and one of the first cohort of Young Academics in the UK (67 recipients nationwide). He has long been engaged in research on smart energy conversion systems and advanced sensing technologies, conducting systematic innovation in areas including new energy, electric vehicles, power electronics, and power semiconductors. He has held teaching and research positions at Zhejiang University (postdoctoral researcher), Newcastle University, the University of Strathclyde, the University of Liverpool, the University of York (founder and head of the Department of Electrical Engineering), and King’s College London.
Title:Resilient Electric Powertrain – A Step Towards Safer and Smarter EVs
Abstract:In this invited talk, Dr. Yihua Hu, Reader at King’s College London and Fellow of IET, presents his research on resilient electric powertrains for electrified transportation. The work addresses critical safety and reliability challenges in modern electric vehicles (EVs) from component, subsystem, and system levels, with the ultimate goal of achieving more reliable, safe, compact, sustainable, and intelligent powertrains.
The talk first examines the dilemma facing the EV industry: how to balance improvements in energy storage density, power density, efficiency, and higher bus voltages against the growing complexity of safety assurance. Dr. Hu then focuses on three key technical challenges.
The first challenge is Sensor Accuracy Uncertainty (SAU). After long-term operation or under harsh conditions, current, voltage, and position sensors may experience accuracy degradation, leading to torque ripple, speed fluctuations, and unbalanced currents. Dr. Hu proposes a multi-sensor correlation model that uses the coupling relationships among sensors to achieve online error estimation and compensation, without requiring complex observers or heavy computation.
The second challenge concerns post-fault operation and limp-home strategies. Using detailed case studies, Dr. Hu reveals the subtle mechanisms that distinguish open-switch faults from open-phase faults, and shows how non-zero currents can persist even after a fault. He then presents hybrid fault-tolerant strategies that combine bus current reconstruction and voltage vector adjustment, enabling the drive system to maintain limited but safe operation when multiple sensors or inverter switches fail simultaneously.
The third challenge is DC bus voltage discharge under emergency conditions. To meet strict safety regulations (e.g., ECE R94), the bus voltage must drop below 60 V within 5 seconds after a crash. Dr. Hu compares pure windingbased discharge, pure resistorbased discharge, and a new hybrid approach. The hybrid method uses a small discharge resistor together with the motor windings, reducing resistor weight by about 64% compared to a pure resistor solution, while achieving a discharge time of less than 4.5 seconds. The talk also explains how piecewise qaxis current control can overcome issues caused by large rotor inertia and small safe current limits.
Throughout the presentation, Dr. Hu emphasises the importance of integrating protection strategies across three levels – precaution, postfault operation, and emergency handling – to create a truly resilient electric powertrain for future electric transportation.
Chongqing University, China
Biography:Yu Wang is a professor and doctoral supervisor at Chongqing University, and the Associate Director of the Academic Committee of the State Key Laboratory of Power Transmission Equipment & System Technology. He has been selected for a national youth talent program. He received his Ph.D. from Nanyang Technological University, Singapore, and previously served as a Marie Curie Research Fellow at Imperial College London. He is an editorial board member of journals such as IEEE Transactions on Smart Grid, Journal of Modern Power Systems and Clean Energy, and IET Generation, Transmission & Distribution. He also serves as a committee member of several academic societies, including the New Energy and Energy Storage Systems Committee of the Chinese Association of Automation, the Smart Microgrid Committee of the China Energy Society, and the Artificial Intelligence and Electrical Applications Committee of the China Electrotechnical Society. He has led projects funded by the National Natural Science Foundation of China (General Program), Chongqing special funds, and the State Grid guide program, among others. His research interests include power system operation and control and smart microgrids. He has published more than 100 SCI/EI papers in related fields, with over 5,000 cumulative citations.
Title:The New Generation Control Paradigm for Smart Microgrids: A Triadic Integration of Knowledge, Data, and Models
Abstract:With the large-scale integration of distributed energy resources into multi-level distribution systems, microgrids and their interconnected systems are gradually evolving into a new form of distribution network with regional autonomy. Such systems are characterized by a large number of devices, complex dynamic behavior, strong operational uncertainty, and significant spatiotemporal coupling, posing unprecedented and severe challenges to the traditional centralized control paradigm based on a single mechanistic model. In recent years, artificial intelligence methods have become a key technical pathway for addressing these challenges. By organically integrating unstructured domain knowledge, multi-source heterogeneous operational data from inside and outside the system, and fundamental physical mechanism models, these methods have shown great potential in tackling the complexity of the new-type power system. This talk will proceed systematically from three perspectives: first, it will analyze typical application scenarios of microgrids and their core control requirements; second, it will explain the necessity of triadic integration of knowledge, data, and models in microgrid control, along with representative technical paradigms; and finally, it will focus on the latest research progress made by our team in networked microgrid cooperative control driven by knowledge-data-model fusion. By constructing a unified control architecture based on knowledge-data-model fusion, this work provides a solid theoretical foundation and a feasible technical pathway for the efficient coordinated operation of distributed source-grid-load-storage systems in the next-generation power system.
Assoc. Prof. Quanfeng Li
Shanghai Dianji University, ChinaBiography: Quanfeng Li is an Associate Professor and Doctoral Supervisor at Shanghai Dianji University, where he serves as the Director of the New Type Motor Development Laboratory. He is a recipient of the Pujiang Scholar Award under the Shanghai Magnolia Talent Program. Dr. Li was a postdoctoral researcher at the University of York, UK, and a visiting scholar at King’s College London, UK. His main research interests include: 1) Key technologies of NVH (Noise, Vibration, Harshness) for electric vehicle powertrain systems; 2) Health condition monitoring technologies for electric drive systems; 3) Design of high-quality novel special motors; 4) NVH characteristics of eVTOL propulsion systems. He has led or participated in over 40 national, provincial, and municipal research projects as well as industrial projects. Dr. Li has published nearly 100 papers in top-tier journals such as IEEE Transactions on Industrial Electronics (TIE), Transactions on Energy Conversion (TEC), Transactions on Transportation Electrification (TTE), and in international conference proceedings. The undergraduate and master's students under his supervision have repeatedly received the Shanghai Outstanding Graduate Award and the National Scholarship.
The lecture began by introducing the phenomenon of "motion sickness" in electric vehicles and analyzing the causes of driving discomfort. Dr. Li focused on three key technologies. The first is the key technology for vibration and noise reduction of the electric motor itself. Starting from the motor’s intrinsic design, he proposed a forward design method that incorporates objective evaluation parameters for high sound quality. The second is the key technology of the transmission path. Dr. Li introduced the vibration inertance matrix method to characterize vibration and noise transfer path characteristics, thereby offering a new approach to reducing vibration and noise through the transmission path. The third is the key technology of human perception. Based on subjective and objective human sensations, this approach accurately characterizes the auditory features of the human ear, establishes new evaluation criteria for powertrain sound quality, and employs advanced artificial intelligence algorithms to enhance the sound quality of the electric drive system.
The report systematically explores how to improve the sound quality of electric vehicle drive systems from three perspectives—the drive unit itself, the transmission path, and human perception—ultimately aiming to create a quieter and more comfortable ride environment, and to drive the evolution of electric vehicle powertrain systems from "noise reduction" to "pleasant sound."
King' s College London, UK
Biography:Yukai Huang graduated from the School of Automation at Huazhong University of Science and Technology in 2020 and received his Ph.D. in Control Science and Engineering from the School of Artificial Intelligence and Automation at Huazhong University of Science and Technology in 2025. He is currently a Research Assistant at King’s College London. His main research interests include high-power power electronic equipment, grid-connected control of power electronics, and stability analysis of new energy power systems.