State of Charge Estimation of Lithium-Ion Batteries Using Adaptive Filtering

Abstract

Adaptive filtering techniques are extensively used to determine online parameters of various uncertain dynamic systems. In this paper, the online open-circuit voltage (OCV) estimation of lithium-ion batteries is proposed by two different adaptive filtering methods, i.e., recursive least square (RLS) and least mean square (LMS). The proposed techniques use the battery's terminal voltage and current to estimate the OCV, which is correlated to state of charge (SOC). Comparison of both methods against an adaptive observer is carried out to validate the proposed estimation scheme. Experimental results highlight the effectiveness of adaptive filtering in online estimation at different charge/discharge conditions and temperatures.