绣山讲坛第413讲-Online Health Monitoring and Optimal Management of Renewable Assets
作者： 已浏览：3次 更新日期：2026-01-05
报告题目：Online Health Monitoring and Optimal Management of Renewable Assets
报告时间：2026年1月8日下午13：30-15：00
报告地点：绣山208会议室
主讲人：Huadong Mo
摘要：
In this talk, the recent advancements in health monitoring and optimal management of renewable energy assets, with a focus on lithium-ion batteries in electric vehicles and energy storage systems will be presented. the advanced battery management system, which integrates state-of-the-art AI and IoT techniques for real-time battery health monitoring and anomaly detection will be introduced. This system addresses key challenges, including data heterogeneity, AI explainability, scalability, data quality, and privacy concerns. Additionally, an overview of our recent contributions to enhancing the robustness of energy systems for net-zero emissions under extreme events, as well as the application of generative AI in industrial settings will be presented.
主讲人简介：
莫华东博士现为澳大利亚新南威尔士大学工程与信息技术学院副教授。他于2012年获得中国科学技术大学自动化学士学位，2016年获得香港城市大学工业工程与工程管理博士学位。他长期致力于利用基于学习的算法提高复杂系统的弹性、性能和安全性的研究，主要涉及电力和能源系统、物理信息系统和制造系统领域，目前已发表了60多篇SCI和会议论文，以及1本专著。担任近10种相关SCI期刊和国际会议的编委会成员，以及IEEE高级成员和IEEE SMC ACT分会主席。
Dr. Huadong Mo joined the University of New South Wales, Australia, as a Lecturer in 2019 and was promoted to Senior Lecturer in 2021 and Associate Professor in 2025. He received the 2024 IEEE SMC Early Career Award, becoming the fourth recipient since its inception in 2014. Additionally, he was awarded the 2023 Visiting Research Fellowship (Pre-award of the Jean d'Alembert Pour Fellowship) in France. Dr. Mo is currently the Coordinator of the Systems Engineering Discipline within the School of Systems and Computing. Previously, he was a Postdoctoral Fellow at the Swiss Federal Institute of Technology, Zurich. He earned a Bachelor's degree in Automation from the University of Science and Technology of China in 2012 and a Ph.D. in Systems Engineering and Engineering Management from the City University of Hong Kong in 2016. Dr. Mo's research focuses on improving the reliability, resilience, performance, and security of complex systems using learning-based algorithms. His work primarily spans power and energy systems, cyber-physical systems, and manufacturing systems, leveraging advanced data collection and analysis techniques to uncover system evolution patterns under uncertainty. Over the years, he has published more than 80 SCI-indexed journal and conference papers, such as in Nature Communications and top SCI journals, along with a monograph. Dr. Mo's research in Australia has secured over 10 million AUD in funding as leading investigator, primarily from the Australian Research Council (ARC) and other government-led initiatives, including Trailblazer, AEA, and the Energy Innovation Fund. Dr. Mo has supervised five HDR students to graduation and currently mentors approximately 30 HDR students. He serves as Associate Editor or an editorial board member for around 10 SCI-indexed journals and international conferences. Additionally, he is a Senior Member of IEEE and the Chair of the IEEE SMC ACT Chapter.
