معرفی
Zijie Feng is a researcher affiliated with Linnaeus University, actively contributing to data-intensive sciences and applications through the Linnaeus University Centre for Data Intensive Sciences and Applications (DISA). His work focuses on developing machine learning workflows for predicting battery remaining useful life (RUL) using historical cycle logs.
- Researcher at Linnaeus University
- Member of DISA (Data Intensive Sciences and Applications)
Research Interests: Zijie Feng specializes in data science methodologies applied to energy storage systems, particularly battery health monitoring. His research integrates machine learning algorithms with data analysis techniques to address challenges in predictive maintenance and lifecycle estimation of batteries. Current projects include workflow-diversified machine learning models for RUL prediction, aiming to advance sustainable energy solutions.
Technological Expertise: The research involves cutting-edge applications of artificial intelligence in engineering contexts, emphasizing open questions in data collection, processing, and utilization for real-world industrial applications.