Correction: Hu, X.; Sun, F. and Zou, Y. Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer. Energies 2010, 3, 1586–1603

Abstract

The authors would like to make the following corrections to their published paper in Energies [1]. [...]

Experimental study on the influence of temperature and state-of-charge on the thermophysical properties of an LFP pouch cell

“…There are several different robust algorithms to accurately estimate the SOC of a lithium-ion battery [49,50]. In this study, the SOC level is set by first finding the full capacity of the test cell.…”

A method for state-of-charge estimation of LiFePO4 batteries based on a dual-circuit state observer

“…There are many conventional approaches exist for determining the SOC, such as OCV model [10], AC-impedance [11], Coulomb counting [12,13], artificial neural networks [14], particle filter [15e17], Luenberger observer [18], Kalman filter [19], sliding-mode observer [20] and other fusion based algorithms [21e23]. OCV is defined as the battery voltage under equilibrium conditions.…”

Predicting the state parameters of lithium ion batteries: the race between filter-based and data driven approaches

“…In this method, mathematical models containing a set of equations are used to predict the parameters of LIBs and provide good accuracy in dynamic battery operating mode. 47 Generally, for LIBs, separate “equivalent models” are designed to mimic the battery's electrical characteristics. On the other hand, estimation models such as electrochemical impedance spectroscopy (EIS), the electrochemical model and filter-based methods are used to predict the battery SOC, SOH & RUL in the real-time domain.…”