A Semi-Empirical Capacity Fade Model for Lithium Ion Cells with Nickel Based Composite Cathode

被引:3
|
作者
Basu, Suman [1 ]
Hariharan, Krishnan S. [1 ]
Kolake, Subramanya Mayya [1 ]
Song, Taewon [2 ]
Yeo, Taejung [2 ]
Doo, Seokgwang [2 ]
机构
[1] Samsung R&D Inst India Bangalore, Computat Simulat Grp SAIT India, Bangalore 560037, Karnataka, India
[2] Samsung Elect, SAIT, Energy Mat Lab, Seoul, South Korea
关键词
COMMERCIAL GRAPHITE/LIFEPO4 CELL; TRANSMISSION ELECTRON-MICROSCOPY; CYCLE-LIFE MODEL; THERMAL-STABILITY; ELEVATED-TEMPERATURES; ACCELERATED CALENDAR; POSITIVE ELECTRODES; DIAGNOSTIC-ANALYSIS; OXIDE DERIVATIVES; FADING MECHANISMS;
D O I
10.1149/2.0841607jes
中图分类号
O646 [电化学、电解、磁化学];
学科分类号
081704 ;
摘要
High energy density and low cost of nickel based composite cathode has drawn significant attention for vehicular application of Li-ion battery. Experimental observations confirm that the capacity fade of such cells are governed by the surface degradation of the cathode particles. Although, there are many experimental studies on this phenomenon, no numerical model exists. In the present investigation a semi-empirical and semi-electrochemical model is proposed based on the experimental results. The cathode degradation based hypothesis is found to be valid on experimental data obtained at varied operating temperature and discharge rates. A physics based model has been derived from the hypothesis. As the cathode degradation reaction is kinetically driven, the capacity-fade model has been decoupled from the standard electrochemistry based battery model, making it computationally economical. Due to this ease of computation, the present approach can be used to estimate the capacity loss due to cathode degradation without detailed electrochemical models and cyclic simulations. The model has shown excellent predictive capability when compared with experimental data for a wide range of operating conditions. Therefore, the model is ideal for an onboard battery management system as well as for a prognostic tool for battery life prediction along with other algorithms. (C) 2016 The Electrochemical Society. All rights reserved.
引用
收藏
页码:A1286 / A1294
页数:9
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