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Member Profile (20)
(Ph.D) S. H. Cho
(Ph.D) S. H. Hong
(Ph.D) M. J. Kang
(Ph.D) J. H. An
(Ph.D) S. H. Lee
(Ph.D) H. J. Yun
(Ph.D) Y. H. Lee
(Ph.D) S. H. Park
(Ph.D) M. J. Kim
(M.S.) B. I. Jeong
(M.S.) J. Y. Byeon
(M.S.) J. K. Phi
(M.S.) G. H. Jeon
(M.S.) H. J. Lee
(M.S.) J. H. Park
(M.S.) K. H. Seong
(M.S.) H. S. Kim
(M.S.) J. H. Lee
(B.S.) Y. J. Lee
(B.S.) M. K. Jeong
Collaborators
(Prof) S. M. Lee
(Prof) S. H. Lee
(Dr. ) J. S. Kwon
(Dr. ) D. K. Lee
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Kihyeon Seong (Master Graduate Student)

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Master Candidate.
AI-Embedded System/Software on Chip (AI-SoC) Platform Lab
School of Electronics Engineering
IT-1, no. 724, Kyungpook National University
Daehak-ro 80, Buk-gu, Daegu, Republic of Korea
Phone: +82 053 940 8648
E-mail: sugiun1018 [@] gmail [DOT] com
[Homepage] [Google Scholar] [SVN]

Repository Commit History

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Introduction

Full Bio Sketch

Mr. Seong is now with is now with SL Advanced Convergence Engineering Center as software designer. He is responsible for designing the BMS (Battery management system) controller software among automotive ECU controllers. He primarily works with Infineon’s AURIX 2ND generation TC375 and TC387 microcontrollers and develops device driver software. In 2012, his graduated with a B.S. degree in in Computer Control Engineering. His research interests are AUTOSAR platform development for automotive software and functional safety and cybersecurity. his goal to focus on developing software that ensures the safe and efficient operation of in-vehicle electronic systems.

Research Topic

Battery Management Systems for Automotive Applications

I am interested in real-time detection of abnormal conditions in battery cells within automotive Battery Management Systems (BMS). Conventional threshold-based detection methods often fail to detect subtle anomalies since they rely only on instantaneous values, leading to false alarms or missed detections. To address these limitations, I studied a statistical lightweight algorithm that incorporates moving average, standard deviation, and z-score to quantify deviations of individual cells from the pack’s overall behavior. In this research, the proposed algorithm was validated using simulation data from a 4S1P battery configuration and then implemented on the Infineon AURIX 2nd generation TC375 Tricore MCU. By leveraging the MCU’s multi-core architecture, the algorithm achieved real-time performance while minimizing computational overhead. Experimental results confirmed that this approach provides earlier and more accurate detection of cell imbalance, thereby enhancing both the safety and reliability of automotive BMS compared to conventional threshold-based methods.

Publications

Journal Publications

Conference Publications (Intl. 1)

  • Kihyeon Seong and Daejin Park. Implementation of a Statistical Lightweight Algorithm-Based Battery Cell Imbalance Detection on Tricore MCU for Automotive BMS In IEEE International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2026) (Under Review), 2026.

Participation in International Conference

  • IEEE ICAIIC 2026, Tokyo, Japan

Last Updated, 2025.09.09