Dr. Yong Min LeeKorea
Yonsei University
| 2026/03 to present | | Associate Dean for Research, School of Engineering, Yonsei Univ. |
| 2024/03 to present | | Professor, Dept. of Chemical and Biomolecular Engineering |
| 2002 - 2007 | | Ph.D., Dept. of Chemical and Biomolecular Engineering, KAIST |
| 2000 - 2002 | | M.S., Dept. of Chemical and Biomolecular Engineering, KAIST |
| 1997 - 2000 | | B.S., Dept. of Chemical Engineering, KAIST |
| 2017/03 - 2024/02 | | Professor, DGIST |
| 2009/09 - 2017/02 | | Professor, Hanbat National University |
| 2007/09 - 2009/08 | | Researcher, SK Energy |
| 2007/02 - 2007/08 | | PostDoc, KAIST |
| 2025 | | Award from the Ministry of Trade, Industry and Resources, Korea |
| 2025 | | PBFC Research Award, Korean Electrochemical Society |
| 2022 | | Award from the Ministry of Science and ICT, Korea |
Digital Twin 3D Battery Modeling and Simulations, Electrode/Cell Design, Evaluation, and Advanced Analysis Techniques, Soft Materials (Binder, Separator, Polymer Electrolyte) for Advanced Batteries
Dr. Yong Min Lee is a Professor in the Department of Chemical and Biomolecular Engineering and the Department of Battery Engineering at Yonsei University, Korea. He earned his Ph.D. in Chemical and Biomolecular Engineering from the Korea Advanced Institute of Science and Technology (KAIST) in 2007. Following his doctorate, he joined SK Innovation Co., where he contributed to the development of large-format lithium-ion batteries for electric vehicles (xEVs). From 2009 to 2017, Dr. Lee served as a tenured professor at Hanbat National University, and subsequently joined the Daegu Gyeongbuk Institute of Science and Technology (DGIST), where he worked as a professor until 2024 before moving to Yonsei University.
Dr. Lee’s research focuses on the design and optimization of composite electrodes and cells for safe, high-performance energy storage systems. His work uniquely integrates digital twin–based modeling with advanced experimental methods to bridge materials, processes, and performance. Recently, his digital twin framework has been extended to 3D battery microstructure modeling and simulation, capturing the evolution of composite electrodes and soft materials—including binders, separators, and polymer electrolytes—for next-generation batteries. His research spans multiple scales, from single particles to full modules, and multiple systems, from lithium-ion to all-solid-state batteries. He has published over 250 peer-reviewed papers and his work has been cited more than 11,000 times. His contributions have been recognized with several distinguished honors, including the Korea Battery Industry Association Prize (2014), an Award from the Ministry of Science and ICT, Korea (2022), and the PBFC Research Award from the Korean Electrochemical Society (2025).
Microstructure-Driven Battery Modeling and Simulations: A New Analysis and Design Tool for All-Solid-State Batteries
TBA TBA
Solid-State Batteries/TBA
All-solid-state batteries are promising next-generation energy-storage systems because of their potential for improved safety and high energy density. However, their electrochemical performance is strongly influenced by the heterogeneous microstructures of composite electrodes, in which active materials, solid electrolytes, conductive additives, binders, and pores form complex three-dimensional networks. In contrast to liquid-electrolyte batteries, insufficient solid-solid contact and discontinuous electronic and ionic pathways can cause severe transport limitations, reaction inhomogeneity, and incomplete active material utilization. Conventional continuum models based on spatially averaged parameters therefore have limited capability to identify these microstructure-dependent phenomena.
In this presentation, a microstructure-driven modeling and simulation framework is introduced as a quantitative analysis and design tool for all-solid-state batteries. Three-dimensional electrode structures are obtained from focused ion beam-scanning electron microscopy (FIB-SEM) and X-ray microscopy or generated using stochastic and physics-informed reconstruction methods. These digital microstructures are subsequently integrated with electrochemical, electronic and ionic transport, and mechanical simulations to establish quantitative relationships among material properties, electrode composition, manufacturing conditions, microstructural architecture, and cell performance.
Microstructure-driven modeling provides more than a visualization of complex electrode structures. It offers a virtual platform for identifying performance-limiting microstructural features, comparing hypothetical architectures, and optimizing electrode composition and processing conditions before extensive cell fabrication. Future integration with artificial intelligence, rapid microstructure generation, and reduced-order surrogate models is expected to enable inverse design and accelerated optimization of all-solid-state battery electrodes. This approach can therefore serve as a new analysis and design tool for developing high-energy, durable, and practically viable all-solid-state batteries.
[References]
1. ACS Energy Letters 9(10) (2024) 5225
2. Advanced Energy Materials 14(2) (2024) 2302596
3. Advanced Energy Materials 13 (2023) 2300172
4. ACS Energy Letters 5(9) (2020) 2995
5. Advanced Energy Materials 10(35) (2023) 2300172