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

• Published in: Batteries (Basel) Vol. 3; no. 2; p. 12
• Main Authors: Chaoui, Hicham, Mandalapu, Sravanthi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2017
• Subjects: Accuracy; Adaptive filters; Algorithms; Comparative studies; Electric vehicles; Filtration; Kalman filters; least mean square (LMS); Lithium; Lithium-ion batteries; Methods; Neural networks; Open circuit voltage; open-circuit voltage (OCV) estimation; Parameter estimation; Parameter uncertainty; Rechargeable batteries; recursive least square (RLS); Recursive methods; State of charge; Studies; Wavelet transforms; Beijing China; United States > US; Asia; China
• ISSN: 2313-0105; 2313-0105
• DOI: 10.3390/batteries3020012

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.