电池(电)
开路电压
内阻
电压
可靠性(半导体)
锂离子电池
锂(药物)
失效机理
降级(电信)
工作(物理)
加速老化
计算机科学
理论(学习稳定性)
材料科学
可靠性工程
电子工程
电气工程
工程类
机械工程
热力学
物理
机器学习
复合材料
功率(物理)
内分泌学
医学
作者
Bin Pan,Dong Dong,Jionggeng Wang,Jianbo Nie,Shuangyu Liu,Yaohe Cao,Yinzhu Jiang
标识
DOI:10.1016/j.electacta.2020.137101
摘要
The whole life aging behavior and degradation mechanism of lithium ion battery (LIB) are critical to ensure the stability and reliability during practical operation. In this work, a new LIB aging modelling and diagnosing method is proposed based on open circuit voltage (OCV) analysis, through a two-stage segmented nonlinear regression algorithm to smooth incremental capacity (IC) curves for the reconstruction of universal OCV curves. Such algorithm can well reserve the chemical features of the IC curves, which enables the quantification of the loss of active materials and lithium inventory in a nondestructive manner with this OCV model-based diagnostic method. A case study of a commercial LiFePO4 (LFP) battery shows a satisfied accuracy of the model, realizing a quick identification of aging behavior and capacity degradation modes, as well as the parameter recognition of the battery internal resistance components.
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