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.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2025.4
- 수록면
- 549 - 560 (12page)
- DOI
- 10.7840/kics.2025.50.4.549
이용수
초록· 키워드
This study presents early results of a web-based digital twin (DT) for battery management systems (BMS). The proposed DT explores a hybrid of model-based and data-driven approaches, enabling the exploitation of each approach’s distinctive merits and constraints. Experiments employing explainable artificial intelligence (XAI) techniques were undertaken to select the most trustworthy and explainable approach to be deployed to a web server. First, a model-based DT was developed using physics based modelling and AI to achieve the hybrid model. Next, four models, including a deep neural network, a long-short-term memory network, a graph neural network (GNN), and a transformer neural network (TNN) model, were independently trained to minimize the residual between the actual battery data and the prediction of the model-based DT. All hybrid DT models were assessed based on mean squared error, latency, and prediction confidence. With the best confidence score of 98.255% and lowest latency of 0.079, the hybrid GNN DT model emerged as the best, demonstrating the viability of the proposed explainable hybrid approach in approximating actual battery behavior and the utility of a web-based DT.
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목차
- ABSTRACT
- Ⅰ. Introduction
- Ⅱ. Methodology
- Ⅲ. Performance Evaluation and Results
- Ⅳ. Conclusions and Future Works
- References
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UCI(KEPA) : I410-151-25-02-092703500
