Battery AI

Research Interest

• Deep neural networks for managing Li-ion batteries 

Education

• Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea, Feb 2025

  • M.S. in Chemical Engineering

  • Thesis: Tranforme-based SoH estimation for lithium-ion batteries across different configurations using distinct health indicators

  • Adviser: Prof. Donghyuk Kim

• Chonnam National University, Gwangju, South Korea, Feb 2023

  • B.S. in Food Science and Technology

Experience

• Practice | 2022, Gwangju Bioenergy R&D Center, Korea Institute of  Energy Research

Publications (# First author, * Corresponding author)

• Paper in progress (1 first-author and 1 co-author)

1. Transformer-based framework for configuration-agnostic Li-ion battery SoH estimation by exploring novel health indicators. Park S#, Kim Y#, Yoo R, Choi Y*, KimD*. Submitted.

2. Interfacial Chemistry-Driven Reaction Dynamics and Resultant Microstructural Evolution in All-Solid-State Batteries. Park C#, Choi J#, Park S#, Kim HJ, Lim G, Kim Y, Jo S, Lee J, Kim J, Lim J, Kim T, Hong J*, Kim D*, Jung SK*. Submitted.

Patents

1. Choi Y, Kim D, Park S, Kim Y, and Yoo R. Machine Learning-based Battery Healt State Estimation Method and Computing Device for Performing The Same. 10-2025-0046943, filed Apr 10, 2025.