yue ren

Dr. yue ren


Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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6 February 2023 in Mathematics
Verified
Journal Article
Open access
PDF file
BLBingyin Lei
Bingyin Lei
YRyue ren
yue ren
HLHuiyu Luan
Huiyu Luan
RDRuonan Dong
Ruonan Dong
XWXiuyuan Wang
Xiuyuan Wang
Kaiye GaoKaiye Gao
Kaiye Gao
Clean and renewable energy is the only way to achieve sustainable energy development, with considerable social and economic benefits. As a key technology for clean and renewable energy, it is very important to research the reliability optimization of microgrids. This paper reviews the research progress in microgrid reliability optimization. This paper first classifies and summarizes the existing research on microgrid control strategies and reliability assessment. Then, the system reliability optimization framework is summarized in terms of both microgrid systems and optimization objectives. Next, we summarize the most commonly used optimization algorithms for microgrid reliability for different microgrid systems. Finally, we provide a bibliometric analysis of the literature on the reliability research of microgrids. In addition, we propose some research challenges in the future for the reliability of microgrids.

23 August 2023 in Energies
Verified
Journal Article
Open access
PDF file
YRyue ren
yue ren
CJChunhua Jin
Chunhua Jin
SFShu Fang
Shu Fang
Li YangLi Yang
Li Yang
ZWZixuan Wu
Zixuan Wu
Kaiye GaoKaiye Gao
Kaiye Gao
Fossil fuel usage has a great impact on the environment and global climate. Promoting new energy vehicles (NEVs) is essential for green and low-carbon transportation and supporting sustainable development. Lithium-ion power batteries (LIPBs) are crucial energy-storage components in NEVs, directly influencing their performance and safety. Therefore, exploring LIPB reliability technologies has become a vital research area. This paper aims to comprehensively summarize the progress in LIPB reliability research. First, we analyze existing reliability studies on LIPB components and common estimation methods. Second, we review the state-estimation methods used for accurate battery monitoring. Third, we summarize the commonly used optimization methods in fault diagnosis and lifetime prediction. Fourth, we conduct a bibliometric analysis. Finally, we identify potential challenges for future LIPB research. Through our literature review, we find that: (1) model-based and data-driven approaches are currently more commonly used in state-estimation methods; (2) neural networks and deep learning are the most prevalent methods in fault diagnosis and lifetime prediction; (3) bibliometric analysis indicates a high interest in LIPB reliability technology in China compared to other countries; (4) this research needs further development in overall system reliability, research on real-world usage scenarios, and advanced simulation and modeling techniques.