Shuo Zhang of The Hong Kong University of Science and Technology (Guangzhou)

With the rapid development of new energy vehicles, power batteries have become key components that directly affect vehicle performance, safety, and lifespan. During long-term operation, batteries are influenced by multiple factors such as temperature variations, load fluctuations, and sensor noise bias. Their internal states are difficult to measure directly, and performance often fluctuates with changing environmental and operating conditions. These uncertainties pose significant challenges for the accurate monitoring and control of battery management systems (BMS). Achieving precise state perception and reliable prediction under complex operating conditions has become essential for ensuring the safety and efficiency of electric vehicles. This presentation focuses on three core aspects—modeling, identification, and estimation—to systematically introduce the research on the on-board power batteries. The main topics include the modeling framework of power batteries, parameter variation recognition during operation, and state estimation and health management strategies for practical applications.

Biography:

Shuo Zhang received the B.S. degree in Electrical Engineering from Harbin University of Science and Technology, Harbin, China, in 2022, and the M.Phil. degree from The Hong Kong University of Science and Technology (Guangzhou) in 2024, where he is currently pursuing the Ph.D. degree. His research interests include modeling, optimization, and control with applications to battery management systems (BMS) and electric motor systems.

Address:China

Yuting Lu of Sun Yat-sen University

Multiphase permanent magnet synchronous machines (PMSMs) are better than conventional three phase PMSMs in terms of efficiency, torque density, and fault-tolerant capability. However, when multiphase PMSM operates under an open-phase fault, the harmonics in the post-fault currents could substantially degrade system efficiency, maximum torque range and maximum speed range. The existing fault-tolerant controls (FTCs) widely consider the current magnitude limit to achieve the extended torque range with minimum loss, while the voltage magnitude limit to achieve extended speed range is not achieved yet. Therefore, FTCs are developed that consider voltage constraints to improve maximum speed with maintaining torque production, and then one further develops the FTC that is applicable to the full torque-speed range with achieving minimum loss. Finally, 11 recently developed FTCs for either surface-mounted or interior machines are evaluated and compared, thereby guiding the design and optimization of FTCs to further improve the control performance and directing the selection of FTCs according to the specific needs of the practical applications.

Biography:

Yuting Lu (Student Member, IEEE) received the B.S. degree in Engineering from Sun Yat-sen University, Shenzhen, China, in 2023. He is currently working toward the M.S. degree with the School of Intelligent Systems Engineering, Sun Yat-sen University, China. His research interests include modeling, optimization, and control of electrical motors.

Address:China

Wenyuan Mi of Harbin Institute of Technology (Shenzhen)

The expanded speed range of modern electric machines requires resolver systems to maintain high position accuracy across a wide spectrum of operating conditions. However, at high speeds, conventional resolver systems are prone to excitation-frequency errors and model inaccuracies, resulting from incomplete removal of excitation signals and an increasing motive voltage component. To address these challenges, a novel complex-vector resolver system is proposed to sustain accuracy throughout the entire speed range. This system features an integrated design of the resolver and its demodulation. And experimental results demonstrate that the proposed approach achieves a mean angle error of less than 0.6 degrees under both standstill and high-speed conditions up to 2 kHz.

Biography:

Wenyuan Mi is a third-year Ph.D. candidate at the Hong Kong University of Science and Technology (Guangzhou). He received his B.Eng. in Electrical Engineering and Automation from the Harbin Institute of Technology, Weihai. Currently, he also serves as a Research Assistant at the Harbin Institute of Technology, Shenzhen. His research interests are electric machines and position sensors.

Yang Shen of The Hong Kong University of Science and Technology (Guangzhou)

This report will introduce a data-driven model-free predictive control (MFPC) technology, which can significantly enhance the control performance of the current loop. By further tapping into the value of data and integrating the nonlinear fitting capability of neural networks, the inner current loop can not only achieve fast tracking but also possess better disturbance rejection capability. Its current tracking speed matches that of model-based predictive control, while enabling a lower current total harmonic distortion. Moreover, compared with the traditional observer-based model-free predictive control, its disturbance rejection effect is more prominent.

Biography:

Yang Shen (Graduate Student Member, IEEE) received the B.Eng. and the M.Eng. degree from the North China University of Technology, Beijing, China, all in electrical engineering in 2018 and 2021, respectively. He is currently pursuing a Ph.D. in the Robotics and Autonomous Systems Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China. His research interests include electric machines and drives, robotic applications of motor drives, and electrified transportation systems.