Modeling and online parameter identification of Li-Polymer battery cells for SOC estimation

Abstract

Finding an accurate and easily to implement model of batteries is an essential step in properly estimating the state of charge (SOC) of the battery in real-time. In this paper, an equivalent circuit based battery model with nonlinear relationship between the open circuit voltage (V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</sub> ) and the SOC is projected into several piece-wise linear functions. Moving window Least Squares (LS) parameter identification technique is then utilized to estimate and update the parameters of the battery model in each sampling time. The continuously updated parameters are fed to a linear observer to estimate the SOC of the battery. The effectiveness of the proposed modeling and estimation approach are verified experimentally on Lithium Polymer batteries.