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.
초록·키워드
Abstract The integration of artificial intelligence (AI) into energy storage prognostics presents a transformative approach for enhancing the safety, reliability, and longevity of next-generation battery technologies. This study introduces a robust temporal deep learning framework for predictive modeling of lithium-metal battery (LMB) degradation, with a focus on AI-driven health forecasting. A comprehensive dataset comprising 23 LMB cells—diverse in capacity, chemistry, and cycling conditions—was curated to train and validate a suite of sequential models including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Transformer networks, and multiple fully connected Deep Neural Networks (DNNs). These were subsequently integrated into a stacked ensemble meta-model (S-DNN) using an Extreme Learning Machine (ELM), designed to enhance forecast accuracy and generalization. The ensemble achieved superior performance with an RMSE of 0.026 Ah, R 2 of 0.9917, and CVRMSE of 0.6955%, outperforming all individual models. Crucially, the framework demonstrated strong early-stage prediction capabilities using only 15% of the cycling data, maintaining a CVRMSE below 6.5%. Rich regression analyses and error visualizations were used to support interpretability and deployment readiness. Limitations related to uniform temperature cycling and the need for broader cross-domain validation are acknowledged as directions for future work. This work advances the frontier of AI for prognostics by introducing an interpretable, generalizable, and ensemble-based architecture for real-time health monitoring in complex electrochemical systems.
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.
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