Model-based condition monitoring for lithium-ion batteries

JOURNAL OF POWER SOURCES

卷:295

Article

Kim, Taesic¹  Wang, Yebin²  Fang, Huazhen³  Sahinoglu, Zafer²  Wada, Toshihiro⁴  Hara, Satoshi⁴  Qiao, Wei⁵ 

[1] Univ Nebraska, Dept Comp Sci & Comp Engn, Lincoln, NE 68588 USA

[2] Mitsubishi Elect Res Labs, Cambridge, MA 02139 USA

[3] Univ Kansas, Dept Mech Engn, Lawrence, KS 66045 USA

[4] Mitsubishi Electr Corp, Adv Technol R&D Ctr, Amagasaki, Hyogo 6618661, Japan

[5] Univ Nebraska, Dept Elect & Comp Engn, Lincoln, NE 68588 USA

关键词: Lithium-ion battery condition monitoring;    Fast upper-triangular and diagonal recursive least squares;    Maximum capacity estimation;    Recursive total least squares;    Smooth variable structure filter;   

DOI  :  10.1016/j.jpowsour.2015.03.184

来源: Elsevier

【 摘 要 】

Condition monitoring for batteries involves tracking changes in physical parameters and operational states such as state of health (SOH) and state of charge (SOC), and is fundamentally important for building high-performance and safety-critical battery systems. A model-based condition monitoring strategy is developed in this paper for Lithium-ion batteries on the basis of an electrical circuit model incorporating hysteresis effect. It systematically integrates 1) a fast upper-triangular and diagonal recursive least squares algorithm for parameter identification of the battery model, 2) a smooth variable structure filter for the SOC estimation, and 3) a recursive total least squares algorithm for estimating the maximum capacity, which indicates the SOH. The proposed solution enjoys advantages including high accuracy, low computational cost, and simple implementation, and therefore is suitable for deployment and use in real-time embedded battery management systems (BMSs). Simulations and experiments validate effectiveness of the proposed strategy. (C) 2015 Elsevier B.V. All rights reserved.

【 授权许可】

Free