Aging mechanism diagnosis of lithium ion battery by open circuit voltage analysis

被引：37
作者：
Pan, Bin [1 ]
Dong, Dong [2 ]
Wang, Jionggeng [2 ]
Nie, Jianbo [3 ]
Liu, Shuangyu [2 ]
Cao, Yaohe [4 ]
Jiang, Yinzhu [1 ]
机构：
[1] Zhejiang Univ, Sch Mat Sci & Engn, State Key Lab Clean Energy Utilizat, Hangzhou 310027, Peoples R China
[2] Zhejiang Huayun Informat Technol Co Ltd, Hangzhou 310008, Peoples R China
[3] State Grid Zhejiang Integrated Energy Serv Co Ltd, Hangzhou 310000, Peoples R China
[4] Zhejiang Zhibei Informat Technol Co Ltd, Hangzhou 310051, Peoples R China
来源：
ELECTROCHIMICA ACTA | 2020年 / 362卷
基金：
中国国家自然科学基金;
关键词：
Lithium ion battery; State of health; Degradation mode; Diagnostic method; OF-HEALTH ESTIMATION; DEGRADATION MODES; CAPACITY LOSS; STATE; PARAMETERS; DEPOSITION; IMPEDANCE; IDENTIFY; CELLS;
D O I：
10.1016/j.electacta.2020.137101
中图分类号：
O646 [电化学、电解、磁化学];
学科分类号：
081704 ;
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. (C) 2020 Elsevier Ltd. All rights reserved.
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页数：8
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