Maximum pulse current estimation for high accuracy power capability prediction of a Li-Ion battery

Abstract

This study gives insight to the design and implementation of the dual extended Kalman filter (dual EKF)-based maximum pulse current estimation for high accuracy power capability prediction of a Li-Ion battery. The information on the lumped resistance, which represents the magnitude of the voltage variance during the predefined time, can be obtained using numerical equations based on two values of the state-of-charge (SOC) and series resistance Ri estimated by the dual EKF. These obtained lumped resistances are properly compared with those extracted by the hybrid pulse power capability prediction (HPPC) technique and the direct current internal resistance (DCIR) technique. Through experimental results that shows little difference between the estimated lumped resistance and those extracted by the HPPC and the DCIR techniques, it can be certainly mentioned that this work sufficiently provides an outstanding solution related to the available maximum pulse current estimation of a Li-Ion battery to be operated within the safety discharging/charging range. Consequently, our proposed dual EKF-based approach is clearly appropriate for providing information regarding the reliable power capability prediction.