​Advanced Battery Management for Emerging Applications

The world is entering a new era of electrification across industry, transportation, energy, and autonomous systems. At the center of this transformation are lithium-ion batteries, which remain the leading energy-storage technology due to their high energy and power density, long cycle life, and broad applicability. As batteries are increasingly deployed in electric vehicles, electric aircraft, grid-scale storage, robotics, and autonomous platforms, the demands placed on their performance, safety, reliability, and lifetime continue to grow.

Advanced battery management is essential to meeting these demands. Systems and control theory can play a central role by enabling rigorous methods for battery modeling, state estimation, health monitoring, diagnostics, prognostics, optimization, and control. By combining physical insight with data-driven tools and control-theoretic guarantees, next-generation battery management systems can support safer, more efficient, and more reliable operation in emerging applications.

This tutorial-style workshop will provide a structured introduction to the state of the art and new frontiers in battery modeling, monitoring, and control. Emphasis will be placed on the integration of electrochemical and physics-based understanding with modern systems, control, and learning-based methods, as well as on practical implementation challenges. The workshop will be particularly relevant to researchers and practitioners in the systems and control community who are interested in developing and applying advanced control-theoretic methods to lithium-ion batteries and emerging battery-powered systems.​

Content Outline

The workshop will walk through a clear set of important topics—from the basics all the way to the latest developments, including:
  • Battery modelling, including electrochemical modeling and equivalent circuit modeling
  • Integrating physics with machine learning for batteries
  • Battery model identifiability and identification
  • Model-based state estimation and fault diagnostics
  • Control-oriented battery system design
  • New architectures for battery systems
  • Optimal power control for batter systems
  • Applications including electric vehicles, electric aircraft, and grid storage ​

Tentative Schedule

First Session
  • Introduction & motivation
  • Fundamentals of modeling
  • Model identification
  • Physics + machine learning

Second Session
  • State estimation and fault diagnostics
  • Battery system design and power control
  • Emerging applications: electric vehicles, electric aircraft, and grid storage)

​Interactive Q&A and discussion 
If you’re curious about topics not listed here or have suggestions, feel free to email Dr. Huazhen Fang at [email protected] or just fill in the form below.

    ​Suggestions for the Workshop?

Speaker: Dr. Huazhen Fang

Dr. Huazhen Fang is an Associate Professor of Mechanical Engineering at Michigan State University. Before coming to MSU, he was on the faculty at the University of Kansas. He earned his Ph.D. from the University of California, San Diego, his M.Sc. from the University of Saskatchewan in Canada, and his B.Eng. from Northwestern Polytechnic University in China.

His research centers on advanced battery systems and control, with projects supported by the National Science Foundation, the Department of Energy, the Army Research Lab, and others. Along the way, he received the NSF CAREER Award, the University Scholarly Achievement Award at the University of Kansas, and a Prize Paper Award from IEEE Transactions on Transportation Electrification.
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He stays active in the research community by contributing various services. He currently serves as an Associate Editor for IEEE Control Systems Letters and IEEE Transactions on Industrial Electronics, and has organized workshops and sessions at ACC, CDC, and several IEEE conferences. His recent roles include Publicity Chair for ACC 2027, General Chair for IEEE ICPS 2024, Program Co‑Chair for the IEEE IES Online Conference 2024, Tutorials Co‑Chair for IECON 2024, and Keynotes Chair for the IEEE ITEC 2023, among others.
Battery Management Is the SciTech of Extracting Performance While Enhancing Safety.
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