Draft Full Paper Due:
June 15, 2024 (Extended)

Notification of Draft Paper Acceptance:
June 25, 2024 (Extended)

Final Manuscript Due:
July 5, 2024 (Extended)

Author Registration Deadline:
July 5, 2024 (Extended)

Regular Attendee Early Bird Registration Deadline:

July 5, 2024 (Extended)


Home > Keynote Speakers
Keynote Speakers

Keynote Speaker


Keynote #1: The Role of Dynamic Modeling of Complex Mechanical System in Sensing and Damage Detection

Dr. Jianqiao Sun, Professor, Department of Mechanical Engineering, University of California, Merced, California, USA

Abstract of the Keynote Talk:

In designing a sensor network for damage detection of complex mechanical systems, it is often difficult to have access to the signals closely correlated to the damage.  Two examples to be considered in this talk are the wheel wear of high-speed train and the bearing internal damage of steel wire drawing machines.  In both cases, it is not possible to place sensors where the wheel wear occurs or where the internal damage occurs of the bearing operating at high speed in high temperature.  We shall take high-speed train as an example and discuss an approach which relies on the dynamic model of the mechanical system and make use of large sets of accessible motion data to estimate the accumulated wear.  We leave the bearing internal damage detection as a challenge to the audience.

Keynote Speaker’s Short Bio:

Dr. Jian-Qiao Sun earned a BS degree in Solid Mechanics from Huazhong University of Science and Technology in Wuhan, China in 1982, a MS and a PhD in Mechanical Engineering from University of California at Berkeley in 1984 and 1988.  He worked for Lord Corporation at their Corporate R&D Center in Cary, North Carolina.  In 1994, Dr. Sun joined the faculty in the department of Mechanical Engineering at the University of Delaware as an Assistant Professor, was promoted to Associate Professor in 1998 and to Professor in 2003. He joined University of California at Merced in 2007 and is currently a professor of the Department of Mechanical Engineering in School of Engineering.  Besides many other editorial experiences, he is the Editor-in-Chief of International Journal of Dynamics and Control published by Springer. His research interests include stochastic non-linear dynamics and control, cell mapping methods, multi-objective optimization, intelligent control systems and high-density piezoelectric energy harvesting from highway traffic.


Keynote #2: Control and Optimisation of Vehicular Electromagnetic Suspension Systems

Dr. Haiping Du, Senior Professor, School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Australia

Abstract of the Keynote Talk:

The suspension system is a critical component of a vehicle's chassis, significantly enhancing ride quality, handling stability, and overall driving performance. However, traditional vehicle suspension systems, which typically employ hydraulic, pneumatic, and mechanical components, face challenges such as sealing issues, slower response speed, and structural complexity. In recent years, electromagnetic suspensions (EMSs) have garnered increasing attention for their ability to circumvent these challenges. EMSs offer numerous advantages, including high response speed, energy efficiency, simple structure, and enhanced controllability. This presentation will explore the latest research developments in the design of electrical networks (EN), energy efficiency optimisation, interconnection architecture, and related control technologies for EMSs. The presentation will cover the following topics: Introduction to the background, composition, and classification of EMS. Presentation of a novel mechanical-electrical analogy theory and the corresponding controllable EN. Optimisation integration design of EMSs, with an emphasis on energy-saving, energy-harvesting, and vibration control characteristics. Comprehensive overview of a novel interconnection architecture for EMS, including research motivation, decoupling structure and control algorithm, and experimental verification. Exploration of the related control technologies and strategies designed for EMS systems. This presentation aims to provide a thorough understanding of EMS advancements and their potential to revolutionise vehicle suspension systems.

Keynote Speaker’s Short Bio:

Haiping Du received the PhD degree from Shanghai Jiao Tong University, Shanghai, China, in 2002. He is a Senior Professor at the School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Australia. He was a Research Fellow with the University of Technology, Sydney, from 2005 to 2009, and was a Postdoctoral Research Associate with Imperial College London from 2004 to 2005 and the University of Hong Kong from 2002 to 2003. He is a Subject Editor of the Journal of Franklin Institute, an Associate Editor of IEEE Transactions on Industrial Electronics, IEEE Transactions on Intelligent Vehicles, IMechE Journal of Systems and Control Engineering, Scientific Reports, SAE Intl Journal of Vehicle Dynamics, Stability and NVH, and IEEE Control Systems Society Conference, an Editorial Board Member of Journal of Sound and Vibration and Journal of Low Frequency Noise, Vibration and Active Control, and a Guest Editor of IEEE Transactions on Mechatronics, IET Control Theory and Application, IET Intelligent Transportation Systems, Mechatronics, Advances in Mechanical Engineering, etc. His research interests include vibration control, vehicle dynamics and control systems, robust control theory and engineering applications, electric vehicles, robotics and automation, smart materials and structures. He is a recipient of the Australian Endeavour Research Fellowship (2012).


Keynote #3: Monitoring and Countermeasure of Dynamics and Vibration Issues in High-Speed Trains

Dr. Xiaoqing Dong, Deputy Director of the Aerodynamics and Vibration and Noise Research Laboratory, Locomotive and Rolling Stock Research Institute, China Academy of Railway Sciences, China

Abstract of the Keynote Talk:

Firstly, the dynamic and abnormal vibration problems encountered during the service of high-speed trains are introduced, then, the mechanism and excitation conditions of abnormal vibration are analyzed from the aspects of wheel/rail relationship, modal matching, and operational conditions. The corresponding monitoring system of China's high-speed railway and corresponding Countermeasures are also illustrated. Finally, by analyzing on-site application cases, the engineering rectification measures of abnormal vibration problems in high-speed railways are presented. In one word, the academic issues related to mechanics that still exist in high-speed railways and potential solutions for the next step of abnormal vibration are presented.

Keynote Speaker’s Short Bio:

Doctor, Researcher, deputy director of the Aerodynamics and Vibration and Noise Research Laboratory, Locomotive and Rolling Stock Research Institute,China Academy of Railway Sciences. Mao Yisheng and Zhan Tianyou Technology Award winners, members of UIC TTI working group, and Specialty Leading Person of China Railway Group.

He has been engaged in wheel/rail interface and vibration-noise research of high-speed railway since 2003. Recently, he also engaged in the quality improvement work of high-speed trains. Up to now, he has led or participated in 4 national projects, led 9 national railway group projects, and over 60 other projects.



Tutorial Speaker


Tutorial: Advances in battery health diagnostics

Dr. Akhil Garg, Associate Professor, School of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST), China

Abstract of the Tutorial:

The rapid advancement of transportation electrification in recent times has been propelled by the widespread evolution of lithium-ion battery technology as a primary energy storage solution. The crucial need of safe and efficient operation of the lithium-ion batteries has positioned the battery management system as a pivotal component in this scenario. Among its various functions, state of health (SoH) monitoring emerges as a paramount function for intelligent battery management. Achieving accurate SoH predictions not only extends the battery lifespan, but also offer significant insights for optimizing battery usage. To cater the aforementioned objectives, we aim to review the significant advancements in battery health diagnostics as well as prognostics using machine learning (ML) techniques that not only enhances the state predictions, but also simultaneously reduces the computational complexity. By delving into the current state of research in this field, this talk aims to elucidate promising future avenues for leveraging ML in the context of batteries. Furthermore, we highlight the increasing necessity for advanced remaining useful life (RUL) prediction techniques and its role in addressing the challenges associated with the surging demand on electric vehicles. This comprehensive review identifies the existing challenges and proposes a structured framework towards enhanced model training process, advanced artificial intelligence (AI) techniques, cloud-edge computing and data-distribution discrepancies. The integration of AI technologies in this endeavor is pivotal to expedite the battery performance and overcome present limitations associated with the health monitoring. In adopting a symmetrical approach, ML synchronizes with the battery management, resulting in the significant contribution to the sustainable progress of transportation electrification. This study provides a concise overview of the literature, offering insights into the current state, future prospects, and challenges in implementing ML techniques for battery health monitoring.

Tutorial Speaker’s Short Bio:

Dr. Akhil Garg is currently working as an Associate Professor at School of Mechanical Science and Engineering at Huazhong University of Science and Technology (HUST), Wuhan, China. He teaches courses on “Battery failures” at China-European Union (EU) Institute of Cleaner and Renewable energy at HUST. His PhD is from School of Mechanical and Aerospace Engineering from Nanyang Technological University, Singapore. During his PhD, he has worked jointly with Aerospace Industry, Rolls-Royce (UK), Singapore for 2 years on Artificial Intelligence and Robust Design optimization from the year 2010-2012. His main research interests include Battery health diagnostics; Air and Liquid cooling system design, Heat transfer, Topology optimization for Mechanical design; Artificial Intelligence for Energy storage Systems. He has been awarded High Level Provincial Talent project in the year 2016 from Guangdong provincial government and Hubei ChuTian title from Hubei Government in the year 2023. Currently, he is presiding the Hubei NSFC, International academic jointly with Queen Mary University of London and St. Petersburg State University of Russia on distributed scheduling and optimizing charging strategy of electric vehicles. He has published over 100 peer reviewed articles in this field and have google scholar H-index of 32 and i-10 index of 85. He is Associate Editor of ASME Journal of Electrochemical Energy Conversion and Storage, Guest Editor of ASME Journal of Computing Information Science and Engineering, Associate Editor of Journal of Intelligent and Fuzzy Systems and Guest Editor of International Journal of Green Energy.