State of Charge Estimators for Lithium-Ion Batteries Based on a Simplified Electrochemical Representation Coupled with an Equivalent Electric Circuit

EasyChair Preprint 15342

Abstract

This article presents a comparative study on the use of the Extended Kalman Filter (EKF) to estimate the State of Charge (SOC) in lithium-ion batteries utilizing two distinct modeling techniques. The first approach combines an Equivalent Electrical Circuit (ECM) with the Coulomb Counting method, while the second integrates a simplified electrochemical model based on the Rakhmatov-Vrudhula (RV) method with the ECM. Both approaches use an Open Circuit Voltage (OCV) vs. SOC function derived from synthetic data obtained through low-current charging and discharging experiments simulated in PyBaMM, a Python-based battery modeling module. The parameters of the ECM and RV models are determined using high-charge pulse discharges and constant discharge experiments, respectively. The performance of each Extended Kalman Filter (EKF) configuration is evaluated using electric vehicle (EV) discharge scenarios such as the Urban Dynamometer Driving Schedule (UDDS), the Highway Fuel Economy Test (HWFET), and the US06 test standard, with errors quantified by the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics, using the SOC calculated electrochemically from PyBaMM as a reference. This study aims to elucidate the accuracy and reliability of each modeling approach, offering insights into their applicability for real-time battery management systems.

Keyphrases: Analytical models, Extended Kalman Filter, PyBaMM, State of Charge, battery modeling

@booklet{EasyChair:15342,
  author    = {Savio Oliveira and Jose Júnior and Matheus Farias and Arthur Alves and Suelen Bampi and Thomas Nunes and Rafael Lima and Antonio Lima},
  title     = {State of Charge Estimators for Lithium-Ion Batteries Based on a Simplified Electrochemical Representation Coupled with an Equivalent Electric Circuit},
  howpublished = {EasyChair Preprint 15342},
  year      = {EasyChair, 2024}}