Spatiotemporal modeling of internal states distribution for lithium-ion battery

被引：16
|
作者：
Wang, Mingliang [1 ,2 ]
Li, Han-Xiong [1 ]
机构：
[1] City Univ Hong Kong, Dept Syst Engn & Engn Management, Kowloon, Hong Kong, Peoples R China
[2] Cent S Univ, State Key Lab High Performance Complex Mfg, Sch Mech & Elect Engn, Changsha, Hunan, Peoples R China
来源：
JOURNAL OF POWER SOURCES | 2016年 / 301卷
关键词：
Lithium-ion batteries; Lower order modeling; Spatiotemporal modeling; Karhunen-Loeve decomposition; Extended Kalman filter; EXTENDED KALMAN FILTER; MANAGEMENT-SYSTEMS; PARAMETER-ESTIMATION; CHARGE ESTIMATION; POLYMER BATTERY; REDUCTION; PACKS; CELLS;
D O I：
10.1016/j.jpowsour.2015.09.107
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
Electrochemical properties of the battery are described in partial differential equations that are impossible to compute online. These internal states are spatially distributed and thus difficult to measure in the battery operation. A space-time separation method is applied to model the electrochemical properties of the battery with the help of the extended Kalman filter. The model is efficiently optimized by using LASSO adaptation method and can be updated through data-based learning. The analytical model derived is able to offer a fast estimation of internal states of the battery, and thus has potential to become a prediction model for battery management system. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：261 / 270
页数：10
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