Description

Prof. Xiaosong Hu's Distinguished Lecture on Advanced Battery Management Algorithms On 14th September 2024, the IEEE Industrial Electronics Society Sri Lanka Chapter hosted a virtual Distinguished Lecture by Prof. Xiaosong Hu, IEEE Fellow and Full Professor at Chongqing University, China. The lecture, titled "Advanced Battery Management Algorithms," attracted about 60 participants, including 40 IEEE members. The session was part of the IEEE Young Professionals Sri Lanka Affinity Group's Y2NPro (Young to Niche Professionals) workshop series, focusing on electric vehicles, and aimed to address key advancements in battery management systems. Prof. Hu began by discussing the research background, highlighting global and domestic challenges such as the energy crisis, environmental pollution, and increasing travel demand. He explained how the transportation revolution, including electric vehicles, aircraft, and ships, creates a strategic demand for more advanced battery technologies. Traction batteries, he noted, present a bottleneck due to safety concerns and performance decay, making advanced battery control systems crucial. The lecture then delved into battery modeling and state estimation, covering electro-mechanical modeling, electro-thermal coupling, and multi-state joint estimation. Prof. Hu addressed battery thermal monitoring and management, focusing on high and low temperatures, thermal non-uniformity, and sensorless temperature estimation, all key to improving battery performance and longevity. Finally, Prof. Hu covered battery health evaluation and fault diagnosis, emphasizing the importance of health evaluation using feature engineering, deep learning models, and lifetime prognostics through semi-supervised learning. He also explored multi-fault detection and safety warnings, offering insights into future battery management solutions. The session was highly engaging, with attendees expressing great interest in the cutting-edge research presented. Prof. Hu's comprehensive overview of advanced battery management algorithms provided valuable knowledge, inspiring further innovation in electric vehicle technologies.