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Batteries 2017, 3(2), 12; doi:10.3390/batteries3020012

Comparative Study of Online Open Circuit Voltage Estimation Techniques for State of Charge Estimation of Lithium-Ion Batteries

1
Department of Electronics, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
2
Department of Electrical and Computer Engineering, Tennessee Technological University, 220 W. 10th Street, Cookeville, TN 38505, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Maciej Swierczynski
Received: 19 January 2017 / Revised: 28 March 2017 / Accepted: 29 March 2017 / Published: 6 April 2017
(This article belongs to the Special Issue Lithium Ion Batteries)
View Full-Text   |   Download PDF [2688 KB, uploaded 6 April 2017]   |  

Abstract

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. View Full-Text
Keywords: lithium-ion batteries; least mean square (LMS); recursive least square (RLS); open-circuit voltage (OCV) estimation lithium-ion batteries; least mean square (LMS); recursive least square (RLS); open-circuit voltage (OCV) estimation
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MDPI and ACS Style

Chaoui, H.; Mandalapu, S. Comparative Study of Online Open Circuit Voltage Estimation Techniques for State of Charge Estimation of Lithium-Ion Batteries. Batteries 2017, 3, 12.

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