Comparative study of online open circuit voltage estimation techniques for state of charge estimation of lithium-ion batteries

被引：28

作者：

Chaoui H. ^{[1
]}

Mandalapu S. ^{[2
]}

机构：

[1] Department of Electronics, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, ON

[2] Department of Electrical and Computer Engineering, Tennessee Technological University, 220 W. 10th Street, Cookeville, 38505, TN

来源：

Batteries | 2017年 / 3卷 / 02期

关键词：

Least mean square (LMS); Lithium-ion batteries; Open-circuit voltage (OCV) estimation; Recursive least square (RLS);

D O I：

10.3390/batteries3020012

中图分类号：

学科分类号：

摘要：

Online estimation techniques are extensively used to determine the parameters of various uncertain dynamic systems. In this paper, online estimation of the open-circuit voltage (OCV) of lithium-ion batteries is proposed by two different adaptive filtering methods (i.e., recursive least square, RLS, and least mean square, LMS), along with an adaptive observer. The proposed techniques use the battery’s terminal voltage and current to estimate the OCV, which is correlated to the state of charge (SOC). Experimental results highlight the effectiveness of the proposed methods in online estimation at different charge/discharge conditions and temperatures. The comparative study illustrates the advantages and limitations of each online estimation method. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.

引用

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