Login / Signup

Advances in performance degradation mechanism and safety assessment of LiFePO 4 for energy storage.

Zhongliang XiaoTaotao ChenTingting ZhaoZhongliang XiaoRongyao YuanCheng LiuGuobin ZhongKaiqi XuQunxuan YanJinfeng CaiXiaoxin PengHaowu Xia
Published in: Nanotechnology (2024)
In the context of 'energy shortage', developing a novel energy-based power system is essential for advancing the current power system towards low-carbon solutions. As the usage duration of lithium-ion batteries for energy storage increases, the nonlinear changes in their aging process pose challenges to accurately assess their performance. This paper focuses on the study LiFeO 4 (LFP), used for energy storage, and explores their performance degradation mechanisms. Furthermore, it introduces common battery models and data structures and algorithms, which used for predicting the correlation between electrode materials and physical parameters, applying to state of health assessment and thermal warning. This paper also discusses the establishment of digital management system. Compared to conventional battery networks, dynamically reconfigurable battery networks can realize real-time monitoring of lithium-ion batteries, and reduce the probability of fault occurrence to an acceptably low level.
Keyphrases
  • mental health
  • machine learning
  • public health
  • risk assessment
  • solid state
  • high resolution
  • deep learning
  • artificial intelligence
  • health information
  • big data
  • data analysis