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Estimation & Control for Battery Management & Ocean Observing

Estimation & Control for Battery Management & Ocean Observing

方华臻博士,美国堪萨斯大学机械工程学系助理教授

  • 时间:2016年11月25日上午10:30
  • 地点:浙大玉泉校区教十八223(智能系统与控制研究所)
  • 联系人:许超,13706711953(手机),53295319(微信)

Abstract

Energy and environment are fundamental challenges facing the human society nowadays. High hopes have been given to a bright future enabled by advanced energy management and environmental monitoring technologies. Recent studies have demonstrated the promise of control theory as a theoretical basis and thinking tool for tackling challenges in the two areas.

In this talk, I will present my research on battery management and ocean observation. The two seemingly distinct problems are inherently related: their solutions share a common foundation in optimal estimation theory. Battery-based energy storage is a major building block of renewable energy facilities and electric vehicles. Maximizing batteries’ operational safety, performance and lifetime largely depends on real-time state-of-charge estimation. For this problem, I will present solution strategies based on adaptive and multi-model estimation. Not only will they reduce the complexity of battery management, but also improve estimation accuracy in the presence of complicated battery dynamics. I will then describe my work on ocean flow field reconstruction via analysis of observation data collected from drifters. The considered problem boils down to nonlinear simultaneous input and state estimation, which will be explored and addressed. This work can help oceanographers understand flows and their impact on transportation of nutrients, motion of biological species, and diffusion of contaminants. In addition, it possesses significant implications for the design of renewable (wind, solar, etc.) energy observation and prediction systems. Such systems play an important role in efficient renewable energy harvesting.

Bio of the Speaker

Huazhen Fang is an Assistant Professor of Mechanical Engineering at the University of Kansas. He received his Ph.D. degree in Mechanical Engineering from the Department of Mechanical & Aerospace Engineering at the University of California, San Diego. Prior to that, he received M.Sc. in Mechanical Engineering from the University of Saskatchewan, Canada (2009), and B.Eng. in Computer Science & Technology from Northwestern Polytechnic University, China (2006). He worked as a Research Intern at Mitsubishi Electric Research Laboratories and NEC Laboratories America, respectively, in 2012 and 2013. His research interests focus on dynamic systems and control with application to energy management, environmental observation and mechatronics. He was selected as a Gordon Engineering Leadership Scholar (2010).