Soo Hee Han
Pohang University of Science and Technology · 工学
研究室紹介
Professor Soo Hee Han's research lab specializes in advanced battery management systems and intelligent estimation techniques for electrochemical energy storage, with a strong focus on state-of-charge (SOC) and state-of-health (SOH) estimation. The lab integrates model-based approaches with data-driven methods such as machine learning and reinforcement learning to enhance accuracy and robustness in real-world applications. It also develops advanced signal processing and filtering techniques, including optimal smoothers and estimators, for dynamic systems with uncertainties. The lab's work supports the development of safer, more efficient electric vehicles and energy storage systems.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15State of health (SOH) is a key issue for saving cost and guaranteeing safety while using a rechargeable battery. Therefore, numerous studies on SOH estimation have been conducted intensively. However, most of the studies need the experimental data for whole lifetime of a battery, and adopt standard charge/discharge pattern that does not reflect the real world driving pattern. For these reasons, it is not suitable to apply the results into battery management system (BMS) of an EV. In this paper,
Battery is one of the most important energy supplement source for our society. Especially, lithium-ion battery has been actively used in various fields such as mobile devices, electric vehicles, or energy storage system. However, a lithium-ion battery has a few life degradation and safety problems, for example, ignition and explosion. Therefore, it is required to observe the inner states of lithium-ion battery consistently to predict or prevent the problems above. Electrochemical model of lithiu
A novel state of charge (SOC) estimation method for lithium-ion batteries is proposed. The method is made by combining a model-based method and a data-driven method. Model-based methods can show acceptable estimation error without large data. However there is a limit in reducing the error because inaccuracy of model still exists. A data-driven method can solve this problem by learning data. The method proposed in this paper optimizes parameters of extended Kalman filter (EKF) with reinforcement
In this paper, an efficient development environment for vertical integration of many tasks involved with robot programming, called Open software Platform for Robotic Services (OPRoS), is presented. It covers from the control of hardware (HW) devices to the execution of complicated application programs. Based on general software (SW) architecture, standardized components with design patterns, frameworks, and servers are offered for developing robot SW applications easily and efficiently. Speciall
In this note, a new type of L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> - E performance criterion for a fixed-lag smoother is introduced, which is given by a gain between the energy of the external disturbances during the recent time horizon and the estimation error at the fixed-delayed time from the current one. By minimizing the maximum value of the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/
본 연구에서는 3차원 지상 레이저 스캐너로부터 취득된 대용량 포인트 클라우드로부터 효과적인 포인트 탐색을 수행하기 위한 인덱싱 방법으로서 3D R-tree와 옥트리를 비교하였다. 포인트 클라우드의 각 포인트로부터 일정 거리 이내의 포인트를 조회하는 방식으로 탐색을 수행하였으며, 탐색 시간 및 메모리 사용량을 측정하였다. 실제 건물과 석탑을 대상으로 취득된 포인트 클라우드에 적용한 결과, 옥트리는 3D R-tree에 비하여 생성 및 탐색 속도가 우수하며 3D R-tree는 보다 메모리 효율적임을 확인할 수 있었다. 3D R-tree는 인덱스 용량과 리프 용량이, 옥트리는 계층 수가 탐색 성능을 좌우함을 확인하였으며, 주어진 자료에 대한 최적의 수치를 도출할 수 있었다. The present study introduces a comparison between 3D R-tree and octree which are noticeable candidates to index large poin