报告题目：Data-driven methods and their applications in fuel cell systems
报 告 人：Dr. Zhongliang LI
主 持 人：赵冬冬 副教授
报告时间：2019年4月12日（周五）下午14:30
报告地点：565net必赢最新版网页254会议室
报告简介：Currently, a large amount of data is being produced and stored during the different system operations. It would be very useful if the behaviors of the systems can be learned from both stored and online acquired data. In addition, data can also be processed to directly design the controller, predict and evaluate system states, and perform real-time optimizations. Data-driven learning and control is playing a more and more important role in today’s automatic control domain. On the other hand, fuel cell systems have posed some research problems in the automatic control field, such as distributed nature, multi-domain and time varying, complex dynamics, multiple time and space scales. This calls for more targeted modelling and control methods and the data-driven ones can be promising alternatives. In this report, some data-driven methods are designed and developed specifically for fuel cell systems. Especially, the data-driven diagnosis, prognosis of fuel cell systems will be talked about.
报告人简历：
Li Zhongliang，法国艾克斯-马赛大学副教授，主要研究领域为燃料电池系统故障诊断及容错控制，近五年在该领域发表论文28篇，其中IEEE、Elsevier顶级SCI期刊论文15篇。主持参与欧盟H2020、法国国家科学研究中心（CNRS）、ANR等项目5项。教育经历：2009年、2011年分别获清华大学电气工程专业学士、硕士学位，2014年获法国艾克斯-马赛大学博士学位。工作经历：2014至2016年法国国家燃料电池实验室（FCLAB）博士后，2016年至今艾克斯-马赛大学副教授。
