Xiao Zhongliang, Chen Taotao, Zhao Tingting, Song Liubin, Yuan Rongyao, Liu Cheng, Zhong Guobin, Xu Kaiqi, Yan Qunxuan, Cai Jinfeng, Peng Xiaoxin, Xia Haowu
School of Chemistry and Chemical Engineering, Changsha University of Science and Technology, Changsha 410114, People's Republic of China.
Southern Power Grid Electricity Science and Technology Co. Ltd, Guangzhou 510180, People's Republic of China.
Nanotechnology. 2024 Apr 30;35(29). doi: 10.1088/1361-6528/ad3bbc.
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(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.
在“能源短缺”的背景下,开发一种新型的基于能源的电力系统对于推动当前电力系统向低碳解决方案发展至关重要。随着锂离子电池用于储能的使用时长增加,其老化过程中的非线性变化对准确评估其性能构成挑战。本文聚焦于用于储能的磷酸铁锂(LFP)的研究,探索其性能退化机制。此外,还介绍了用于预测电极材料与物理参数之间相关性、应用于健康状态评估和热预警的常见电池模型以及数据结构和算法。本文还讨论了数字管理系统的建立。与传统电池网络相比,动态可重构电池网络能够实现对锂离子电池的实时监测,并将故障发生概率降低到可接受的低水平。