March 28, 2024, 4:41 a.m. | Guangzai Ye, Li Feng, Jianlan Guo, Yuqiang Chen

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.18379v1 Announce Type: new
Abstract: Accurately estimating the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for maintaining the safe and stable operation of rechargeable battery management systems. However, this task is often challenging due to the complex temporal dynamics involved. Recently, attention-based networks, such as Transformers and Informer, have been the popular architecture in time series forecasting. Despite their effectiveness, these models with abundant parameters necessitate substantial training time to unravel temporal patterns. To tackle these challenges, we …

abstract architecture arxiv attention batteries battery cs.ai cs.lg dynamics however life lithium lithium-ion batteries management networks prediction systems temporal transformers type

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Director, Clinical Data Science

@ Aura | Remote USA

Research Scientist, AI (PhD)

@ Meta | Menlo Park, CA | New York City