Speaker: Seongbeom Lee from Stanford University
Seminar Abstract:In this presentation, optimal participation strategies of physics-based battery models in grid applications will be discussed in two aspects. First, the systematic approach to characterization of the grid-specific duty cycles will be presented, which can create synthetic duty cycles to mimic grid-specific battery dynamic behaviors. The generation of synthetic duty cycles has the potential to facilitate laboratory testing while maintaining the same characteristics of grid duty cycles. Second, the robust and sleek MATLAB® implementation will be introduced. This MATLAB® framework allows users to simulate physics-based battery models as well as identify model parameters directly after numerical discretization, using a standard solver provided by MATLAB® environment. In addition, some examples will be discussed how physics-based battery models can maximize the safety and usability of the battery systems. For example, electric vehicle (EV) batteries are expected to retire when their capacity has decreased to 70-80 percent of their initial value. Repurposing retired EV batteries to be used in stationary grid applications can increase the total device lifetime value. The optimal deployment based on the accurate state of health of the retired EV batteries can be achieved by physics-based battery models.
Speaker Bio: Seongbeom Lee is a postdoctoral research fellow in the Department of Energy Resources Engineering at Stanford University (Advisor: Prof. Simona Onori). His research aims to create the very first, transformative and computationally optimized modeling framework for electrochemical energy storage systems that is able to accurately describe macroscale (both in 1-D and 2-D) dynamics used for design optimization and systematically transfer the accuracy and fidelity of such macroscale models to low-order/reduced models suitable for real-time control.Seongbeom received a B.E. in Chemical Engineering and a Master’s in Chemistry from Sogang University, Seoul, South Korea. He also earned a Ph.D. in Chemical Engineering from the University of Washington, Seattle. Seongbeom has been working on experiments, modeling, and simulations of electrochemical energy storage and conversion systems, and as a result, he has authored 5 peer-reviewed publications, combining material science, electrochemistry, computer science (e.g., machine learning), and mathematical techniques. This multidisciplinary expertise/experience enabled him to collaborate with many research groups, minimizing a collaboration time delay between modelers/controllers and experimentalists.
Seminar is open to all Stanford students, faculty and staff. Register via the RSVP link.