News & Highlights

Invited talk — "Impact-Induced Damage Assessment and Remaining Useful Life Prediction of Lithium-Ion Cells Using Acoustic Emission Features" (Physical AI and PHM track). June 24–27, 2026, Westin Chosun Busan Hotel, Busan, Republic of Korea.

"Characterizing critical failure impact on LIB pack stability via multiphysics response" (Corresponding Author). Multiphysics FEM investigation of battery pack behavior under impact — revealing how SOC level and pack configuration interact to determine both mechanical integrity and electrochemical stability.

"Advancing battery prognostics and health management: Challenges and future perspectives" (First Author). A systematic review of battery PHM research paradigms — physics-based, data-driven, and hybrid — co-authored with Prof. Heung Soo Kim at KAIST. Identifies the "homogenization bottleneck" and proposes future directions including digital twins and explainable AI.

"Failure Modes and Failure Energy Threshold of Lithium-ion Batteries under Extreme Impact" (Corresponding Author). Systematic characterization of LIB failure mechanisms across quasi-static, drop-weight, and ballistic loading regimes — establishing critical energy thresholds and loading-rate effects on internal damage.

"Deep learning-based real-time damage assessment of lithium-ion batteries under dynamic impact" (Corresponding Author). CNN-BiLSTM framework with acoustic emission signals achieves 98.3% accuracy in classifying four levels of internal battery damage — enabling real-time non-invasive health monitoring.

Parts 1 & 2 of "Determination of dynamic flow stress equation based on discrete experimental data" (First Author, with Q.M. Li). Completing the foundational trilogy on data-driven constitutive modeling — Part 1 addresses varied strain-rate SHPB methodology; Part 2 extends to the full 3D (strain × strain-rate × temperature) constitutive surface.

"Determination of residual velocity model of finite thickness material based on Artificial Neural Network" (Corresponding Author). ANN + SVD/CP framework generalized from ballistic limit to residual velocity prediction, covering multi-parameter impact scenarios.

Started postdoctoral position at InnoCORE PRISM-AI Center, Korea Advanced Institute of Science and Technology (KAIST), working on AI-driven prognostics and health management of lithium-ion batteries. Supervised by Prof. Heung Soo Kim.

Completed doctoral degree in Mechanical Engineering from the University of Manchester under the supervision of Prof. Qingming Li. Thesis on data-driven methods for dynamic material constitutive characterization.

"Determination of ballistic resistance model of finite thickness material based on artificial neural network" (Corresponding Author). ANN-based ballistic limit velocity model accounting simultaneously for target thickness, impact angle, and projectile nose shape.

"Multiscale modeling for the impact behavior of 3D angle-interlock woven composites." Subcell-based discretization scheme bridging micro/meso/macroscales for ballistic impact simulation of woven composite structures.