Skip to main content

Ki‐Yong Oh

Hanyang University · 工学

研究室紹介

Professor Ki-Yong Oh's research lab specializes in the development of advanced monitoring systems and physics-informed modeling for sustainable energy systems and battery safety. The lab focuses on structural health monitoring of wind turbine components, offshore wind foundation design considering soil-structure interaction, and predictive modeling of thermal runaway in lithium-ion batteries using multiphysics-informed neural networks. Key research directions include real-time condition monitoring, failure detection in renewable energy infrastructure, and the integration of physical laws with data-driven algorithms for improved accuracy and reliability.

offshore wind foundationsthermal runaway predictionmultiphysics-informed neural networksstructural health monitoringlithium-ion battery safety

Research Overview

Papers
168
Total Citations
2,998
Papers (5y)
80
Primary Field
工学

Research Output Trend

Figures are computed from collected data and may differ slightly.

Publications per year (5y)
80total
2022
2023
2024
2025
2026
Citations per year (5y)
647total
20222023202420252026

Selected Papers

15
1
Review|274 citations·2018
A review of foundations of offshore wind energy convertors: Current status and future perspectives
Ki‐Yong Oh, Woochul Nam, Moo Sung Ryu, Ji-Young Kim, Bogdan I. Epureanu
SJR Q1Renewable and Sustainable Energy ReviewsOA

This paper reviews foundations for offshore wind energy convertors considering the significant growth of offshore wind energy since the early 2000s. The characteristics of various foundation types (i.e., gravity, pile, suction caisson, and float type) and the current status of field application are discussed. Moreover, the mechanical characteristics of soil are described in the sense that these characteristics including modulus, strength, damping, and modulus degradation of soil play critical ro

Civil and Structural EngineeringEngineering
2
Article|202 citations·2014
Rate dependence of swelling in lithium-ion cells
Ki‐Yong Oh, Jason B. Siegel, Lynn E. Secondo, Sun Ung Kim, Nassim A. Samad, Jiawei Qin, Dyche Anderson, Krishna Garikipati, Aaron Knobloch, Bogdan I. Epureanu, Charles W. Monroe, Anna G. Stefanopoulou
SJR Q1Journal of Power Sources
Automotive EngineeringEngineering
3
Article|121 citations·2021
Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features
Seho Son, Siheon Jeong, Eunji Kwak, Jun‐Hyeong Kim, Ki‐Yong Oh
SJR Q1Energy
Automotive EngineeringEngineering
4
Article|110 citations·2012
An assessment of wind energy potential at the demonstration offshore wind farm in Korea
Ki‐Yong Oh, Ji-Young Kim, Jae-Kyung Lee, Moo-Sung Ryu, Jun-Shin Lee
SJR Q1Energy
Aerospace EngineeringEngineering
5
Article|102 citations·2015
A novel thermal swelling model for a rechargeable lithium-ion battery cell
Ki‐Yong Oh, Bogdan I. Epureanu
SJR Q1Journal of Power SourcesOA
Automotive EngineeringEngineering
6
Article|97 citations·2016
Phenomenological force and swelling models for rechargeable lithium-ion battery cells
Ki‐Yong Oh, Bogdan I. Epureanu, Jason B. Siegel, Anna G. Stefanopoulou
SJR Q1Journal of Power SourcesOA
Electrical and Electronic EngineeringEngineering
7
Article|80 citations·2015
A Novel Method and Its Field Tests for Monitoring and Diagnosing Blade Health for Wind Turbines
Ki‐Yong Oh, Joon-Young Park, Jun-Shin Lee, Bogdan I. Epureanu, Jae-Kyung Lee
SJR Q1IEEE Transactions on Instrumentation and Measurement

A new diagnostic method is proposed to efficiently monitor the structural health and detect damages in wind turbine blades. A high-resolution real-time blade condition monitoring system that considers the harsh turbine operating environment and uses optical sensors and a wireless network is presented. A hybrid algorithm, which merges probabilistic analysis, design loads, and real-time load estimates, is introduced to enhance operational safety and reliability. Moreover, the alarm limits are upda

Civil and Structural EngineeringEngineering
8
Article|69 citations·2011
Wind resource assessment around Korean Peninsula for feasibility study on 100 MW class offshore wind farm
Ki‐Yong Oh, Ji-Young Kim, Jun-Shin Lee, Ki-Wahn Ryu
SJR Q1Renewable Energy
Aerospace EngineeringEngineering
9
Article|65 citations·2023
A novel physics-informed neural network for modeling electromagnetism of a permanent magnet synchronous motor
Seho Son, Hyunseung Lee, Dayeon Jeong, Ki‐Yong Oh, Kyung Ho Sun
SJR Q1Advanced Engineering Informatics
Statistical and Nonlinear PhysicsPhysics and Astronomy
10
Article|64 citations·2023
Modeling and prediction of lithium-ion battery thermal runaway via multiphysics-informed neural network
Sung Wook Kim, Eunji Kwak, Jun‐Hyeong Kim, Ki‐Yong Oh, Seung‐Chul Lee
SJR Q1Journal of Energy StorageOA

In this study, a multiphysics-informed neural network (MPINN) is proposed for the estimation and prediction of thermal runaway (TR) in lithium-ion batteries (LIBs). MPINNs are encoded with the governing laws of physics, including the energy balance equation and Arrhenius law, ensuring accurate estimation of time and space-dependent temperature and dimensionless concentration in comparison to a purely data-driven approach. Specifically, the network is trained using data from a high-fidelity model

Automotive EngineeringEngineering
11
Article|61 citations·2022
Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries
Sung Wook Kim, Ki‐Yong Oh, Seung‐Chul Lee
SJR Q1Applied Energy
Automotive EngineeringEngineering
12
Article|61 citations·2016
Characterization and modeling of the thermal mechanics of lithium-ion battery cells
Ki‐Yong Oh, Bogdan I. Epureanu
SJR Q1Applied EnergyOA
Automotive EngineeringEngineering
13
Article|58 citations·2016
A novel phenomenological multi-physics model of Li-ion battery cells
Ki‐Yong Oh, Nassim A. Samad, Youngki Kim, Jason B. Siegel, Anna G. Stefanopoulou, Bogdan I. Epureanu
SJR Q1Journal of Power SourcesOA
Automotive EngineeringEngineering
14
Article|51 citations·2017
A phenomenological force model of Li-ion battery packs for enhanced performance and health management
Ki‐Yong Oh, Bogdan I. Epureanu
SJR Q1Journal of Power SourcesOA
Automotive EngineeringEngineering
15
Article|50 citations·2024
Prediction of thermal runaway for a lithium-ion battery through multiphysics-informed DeepONet with virtual data
Jinho Jeong, Eunji Kwak, Jun‐Hyeong Kim, Ki‐Yong Oh
SJR Q1eTransportation
Automotive EngineeringEngineering

Research Areas

Automotive EngineeringCivil and Structural EngineeringControl and Systems EngineeringMechanical EngineeringAerospace EngineeringElectrical and Electronic Engineering

Ki‐Yong Ohの研究をNubintでさらに深く

この研究室の論文をアプリで開き、AIと共に読み、要約し、引用しましょう。