인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Large-Scale energy storage systems (ESSs) require quantitative methods for evaluating cell voltage inconsistency using operational data. In a battery rack composed of numerous series-connected cells, differences in state of charge, capacity, internal resistance, and degradation can result in different voltage behaviors even under the same operating conditions. This study proposes a quantitative method for evaluating relative cell voltage inconsistency based on Robust Principal Component Analysis (RPCA), subspace projection, and Mahalanobis Distance (MD). RPCA was applied to decompose the cell voltage matrix into a low rank component representing the common voltage variation shared among cells and a sparse component representing localized deviations. Singular Value Decomposition was then applied to the low-rank component to extract the dominant temporal subspace, and the original voltage trajectory of each cell was projected onto this subspace to obtain representative charging and discharging features. The two features were combined into a two-dimensional feature vector, and MD was calculated to quantify the relative deviation of each cell from the overall cell population. The proposed method was applied to four battery racks, each consisting of 238 cells, over three operating cycles under identical state-of-charge ranges and constant-power conditions. The results showed that most cells exhibited similar MD levels, whereas several cells repeatedly showed relatively high MD values across different cycles. Comparison of their original voltage trajectories confirmed persistent differences in charging and discharging behavior relative to cells with low MD values. In addition, many high-MD cells were located at the same positions within different modules, indicating that structural, measurement, wiring, or balancing effects should also be considered when interpreting the results. Therefore, the proposed MD-based index is more appropriately interpreted as a screening indicator for identifying cells with relatively distinct voltage behavior rather than as a direct indicator of cell degradation or failure.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
