A Moving Window Least Mean Square Approach to State of Charge Estimation for Lithium Ion Batteries

Abstract

A novel Lithium Ion (Li-Ion) battery model parameter identification technique based on a simple on-line adaptive approach is presented in this paper. The proposed technique is able to accurately estimate the State of Charge (SOC) in Li-Ion batteries by a very simple manner. A previously proposed Li-Ion battery model and its dynamical equations have been used to develop the proposed parameter estimation algorithm. Estimated model parameters are used to calculate the Open-Circuit Voltage (OCV) that is employed to determine the SOC with no advanced knowledge of the battery parameters. Furthermore, the paper introduces a moving window least mean square approach that adaptively updates estimated battery in a very fast manner. The SOC is recalculated at the end of each window cycle based on the newly estimated parameters. The proposed SOC estimation approach continuously tracks any changes in the battery/model parameters and is fast, accurate, and simple.