A novel online adaptive fast simple state of charge estimation for Lithium Ion batteries

Abstract

This paper proposes a novel simple adaptive and online approach to estimate the state of charge (SOC) in Lithium Ion (Li-Ion) batteries based on a new model parameter identification method. First, a novel discrete model for the Li-ion battery is developed. This model is the key step in the development of the proposed parameter estimation algorithm. The estimated parameters are used for on-line calculation of the battery's open circuit voltage (VOC) that is required for SOC estimation with no prior knowledge of battery parameters. The paper then proposes a moving window lease mean square approach to adaptively update the estimated parameters in a very fast and accurate manner. The SOC estimation will be updated at the end of every window cycle. The proposed method for SOC estimation provides a simple, fast, comprehensive, and precise estimation capable to track the changes of the model/battery parameters. Unlike other estimation strategies, only battery terminal voltage and current measurements are required.