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Eunsong Kim,Minseon Kim,Juo Kim,Joonchul Kim,Jung-Hwan Park,Kyoung-Tak Kim,Joung-Hu Park,Taesic Kim,Kyoungmin Min 한국정밀공학회 2023 International Journal of Precision Engineering and Vol.24 No.7
Lithium-ion batteries are widely used in electric vehicles, electronic devices, and energy storage systems owing to their high energy density, long life, and outstanding performance. However, various internal and external factors affect the battery performance, leading to deterioration and ageing. Accurately estimating the state of health (SOH), state of charge (SOC), and remaining useful life (RUL) of batteries is challenging owing to complex operating characteristics and changing internal physical parameters. With the increasing availability of shared battery data and improved computer performance, the use of data-driven methods for battery health estimations and RUL predictions has gained popularity. We provide a comprehensive review of several studies in which data-driven methods were used for SOC and SOH estimation and RUL prediction. Specifically, we focus on the importance of open battery-cycling databases, various prediction methods used, and results obtained using each of these methods. Moreover, we aim to facilitate further research by providing a comprehensive description of the current state-of-the-art methods employed in battery health estimation and RUL prediction using open databases and machine-learning algorithms. Thus, we hope that this review will help researchers to develop accurate and reliable predictive models for battery health assessment in the future.
Joonchul Yun,Hyungon Lyu,Jinwoo Kim,Wonseog Koo,Shingyu Kim 한국자기학회 2021 한국자기학회 학술연구발표회 논문개요집 Vol.31 No.2
This study has developed technology for manufacturing process of soft magnetic iron powder for eco-friendly automotive part application. Especially, this study investigated the effect of particle size distribution and impurity contents on the magnetic properties of iron powder. The iron based soft magnetic powder was prepared by fluidized bed process using water atomized iron powder, phosphoric acid solution and additional insulation materials. The measurement of magnetic property revealed that the iron based soft magnetic powder had a magnetic flux density of 1.5~1.6T and core loss of 140~200W/kg at frequency of 1kHz. It is expected that the magnetic properties of soft magnetic iron powders can be improved by follow research which is controlling the insulation coating process based on this study.
Decoupling Method Between Digital Signals on FPCB and Mobile Handset Antenna
Joonchul Kim,김형동 한국전자통신연구원 2011 ETRI Journal Vol.33 No.1
Digital harmonics, which may reduce the radio frequency sensitivity of a system, can be coupled with an antenna in a mobile handset. This paper presents a decoupling method for increasing the isolation between digital harmonics on a flexible printed circuit board (FPCB) and an antenna in terms of the ground mode current and the concept of reaction. We model the signal and ground lines in an FPCB as a loop circuit exciting a ground mode current and demonstrate a simple but efficient decoupling method for reducing the excited ground mode current.
기계학습을 이용한 신규 Double Perovskite 스크리닝
김준철(Joonchul Kim),민경민(Kyoungmin Min) 대한기계학회 2020 대한기계학회 춘추학술대회 Vol.2020 No.12
Double perovskite structures have brought a lot of attention due to their great potentials for applications of batteries, lighting-devices, and energy harvesting materials. In this study, machine learning algorithm is employed to search for new stable double perovskite materials. First, the materials properties are adopted from well-established Materials Project database to develop a prediction model for the formation energy and the convex hull energy. Then, the bagging and boosting based algorithms are implemented to train the database for regression as well as classification model and their prediction accuracy is compared. For prediction of the formation energy, it’s R2 and RMSE value reaches to 0.97 and 0.2020 eV/atom. In addition, the classification accuracy for the convex hull energy shows 0.76 with F1-score of 0.763. Finally, trained machine learning model is applied to the whole chemical space of the double perovskite structures and it exhibits that 8,613 structures are potentially stable to be synthesized. In the meanwhile, 25,062 and 19,766 structures are shown to be metastable and instable, respectively.
Molecular Dynamics simulation을 이용한 3D printed zeolite 구조체의 미시적, 거시적 기계적 거동 분석
김준철(Joonchul Kim),민경민(Kyoungmin Min) 대한기계학회 2023 대한기계학회 춘추학술대회 Vol.2023 No.11
Mechanical properties are important properties from atomic unit structures to micro-level structures. Discovering the correlation between structural information and mechanical reactions can accelerate the structure design with desired mechanical properties. By establishing a link between mechanical properties and structural information, we prove the possibility of purposeful structural design. We focus on uncovering resemblances in mechanical behavior between atomistic arrangements and 3D-printed zeolite structures. Employing molecular dynamics simulations, we validate the emergence of similar mechanical responses at both atomic and macroscopic scales. 3D printed structure using thermoplastic polyurethane (TPU) filaments can reflect the response of microstructural-level simulations, which can link between experiment and theory. These results demonstrate that the design can realize meta-materials with ideal mechanical reactions based on theoretical results of atomic structures.
머신 러닝을 활용한 고엔트로피 가넷 구조의 전고체 전지를 위한 고체 전해질 발견
선지원(Jiwon Sun),김은송(Eunsong Kim),김주오(Juo Kim),김준철(Joonchul Kim),민경민(Kyoungmin Min) 대한기계학회 2023 대한기계학회 춘추학술대회 Vol.2023 No.11
Research in all-solid-state batteries (ASSBs) has surged due to their improved energy density and safety over liquid-based lithium-ion batteries. To enhance electrochemical performance, stable solid-state electrolytes (SSEs) are crucial. This study employed a novel machine learning (ML) screening platform to explore 161,280 high-entropy (HE) garnet-type SSE originated from known Li₇La₃Zr₂O<SUB>12</SUB> (LLZO) structures. Initially, an ML-based surrogate model identified electronconductive (bandgap < 1 eV) and thermodynamically unfavorable (energy above hull > 0.035 eV) materials to prevent short-circuits and decomposition. A recently developed ML interatomic potential (M3GNet) guided atomic arrangement for stability. Elastic properties were predicted to ensure dendrite suppression and interfacial stability. Molecular dynamics using M3GNet confirmed Li diffusivity. In conclusion, 23 promising HE garnet materials were identified for advanced ASSBs with these methods.