恒流
健康状况
常量(计算机编程)
电压
电池(电)
可靠性
过程(计算)
时间常数
荷电状态
锂(药物)
计算机科学
控制理论(社会学)
可靠性工程
功率(物理)
电气工程
工程类
物理
内分泌学
人工智能
程序设计语言
控制(管理)
操作系统
医学
量子力学
作者
Zengkai Wang,Shengkui Zeng,Jianbin Guo,Taichun Qin
出处
期刊:Energy
[Elsevier]
日期:2018-11-07
卷期号:167: 661-669
被引量:150
标识
DOI:10.1016/j.energy.2018.11.008
摘要
State of health estimation is critical for ensuring the safety and dependability of lithium-ion batteries. In practical usage, batteries are seldom completely discharged. With a constant current-constant voltage charging mode, the incomplete discharging process influences the initial charging voltage and the charging time of the subsequent constant current charging, greatly hindering the applications of many traditional health indicators that require a full cycling process. However, the charging data of the constant voltage charging is fully reserved, and is not affected by the previous incomplete discharging process. Furthermore, the charging current curve during the constant voltage profile is discovered to relate with the battery state of health in this study. Therefore, a new health indicator is extracted only from the monitoring parameters of the constant voltage profile for state of health estimation. The battery aging phenomena during the constant voltage profile are firstly characterized by the equivalent circuit model, and a new indicator is then constructed. A framework for the online extraction of this indicator of is proposed. Additionally, the correlation analysis and performance assessment prove the adaptability and effectiveness of the proposed method for estimating state of health of lithium-ion batteries.
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