Lecture Information

【Speakers】

Dr. Feng Guo

【Moderator】

Professor Shiqi Shawn Ou

【Presentation Title】

Electrochemical Model-Driven Advanced Battery Management: Modeling, Sensing, and Optimization

【Date and Venue】

Date: October 8, 2026, 3:00–4:00 p.m.

Venue: Online (Tencent Meeting: 966-388-827)

Expert Introduction

Bio: Dr. Feng Guo is an FWO Senior Postdoctoral Researcher funded by the Research Foundation – Flanders (FWO), Belgium, and is currently conducting research on advanced battery management at the University of Warwick, UK. He is also an IEEE Senior Member. His research focuses on control-oriented electrochemical modeling, battery state estimation, parameter identification, fault diagnosis and fault-tolerant control, and physics-guided artificial intelligence, with particular emphasis on the computational efficiency, robustness, and engineering deployability of electrochemical models. His research has been published in leading journals including Energy Storage Materials, Applied Energy, Journal of Energy Chemistry, Energy, and Journal of Energy Storage.

Abstract of the Presentations

Presentation Title: Electrochemical Model-Driven Advanced Battery Management: Modeling, Sensing, and Optimization

Abstract：Electrochemical models can describe mass transport, reaction, polarization, and thermal processes inside lithium-ion batteries at a mechanistic level, thereby providing rich physical information for battery state estimation, performance prediction, safety monitoring, and operation optimization. However, their high computational complexity, large number of parameters, and strong coupling, together with parameter variations, sensor errors, and model mismatch under real operating conditions, still constrain their application in online battery management systems.

This presentation will focus on electrochemical models and their applications for advanced battery management. First, model reduction, parameter grouping, sensitivity analysis, and numerical acceleration methods tailored to control and online computation requirements will be introduced. Efficient and robust model parameter identification strategies will then be discussed to improve model applicability under different operating conditions and battery states.

On this basis, battery state estimation, anomaly monitoring, and fault diagnosis methods based on electrochemical models will be presented, and the integration of physical models with data-driven methods will be explored to improve estimation accuracy and robustness under complex operating conditions and model uncertainty. Subsequently, in the contexts of fast charging, operating boundary constraints, and energy storage system optimization, the extension of electrochemical models from state perception to operation decision-making and control will be discussed. Finally, an integrated research framework of “experimental characterization—mechanistic modeling—parameter identification—state estimation—fault diagnosis—optimal control” will be envisioned, providing a physical foundation for next-generation intelligent battery management.