Pohang University of Science and Technology · Engineering
Professor Soohee Han's research lab specializes in advanced battery management systems and intelligent estimation techniques for electrochemical energy storage systems, 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 under real-world operating conditions. It also develops efficient software platforms for robotics and control systems, emphasizing real-time performance, system integration, and simulation-driven development. The lab’s work bridges theoretical modeling with practical applications in electric vehicles, energy storage, and autonomous systems.
Figures are computed from collected data and may differ slightly.
State 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
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