Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO2/LiFePO4/LiNiMnCoO2Cells

Abstract

Lithium rechargeable cells are used in many industrial applications, because they have high energy density and high power density. For an effective use of these lithium cells, it is essential to build a reliable battery management system (BMS). Therefore, the state of charge (SOC) estimation is one of the most important techniques used in the BMS. An appropriate modeling of the battery characteristics and an accurate algorithm to correct the modeling errors in accordance with the simplified model are required for practical SOC estimation. In order to implement these issues, this approach presents the comparative analysis of the SOC estimation performance using equivalent electrical circuit modeling (EECM) and noise suppression technique (NST) in three representative LiCoO₂/LiFePO₄/LiNiMnCoO₂ cells extensively applied in electric vehicles (EVs), hybrid electric vehicles (HEVs) and energy storage system (ESS) applications. Depending on the difference between some EECMs according to the number of RC-ladders and NST, the SOC estimation performances based on the extended Kalman filter (EKF) algorithm are compared. Additionally, in order to increase the accuracy of the EECM of the LiFePO₄ cell, a minor loop trajectory for proper OCV parameterization is applied to the SOC estimation for the comparison of the performances among the compared to SOC estimation performance.