Model-based state of charge estimation algorithms under various current patterns
Paper i proceeding, 2019

© 2019 The Authors. Published by Elsevier Ltd.This is an open access article under the CC BY-NC-ND license ( under responsibility of the scientific committee of ICAE2018 The 10th International Conference on Applied Energy. Numerous model-based techniques have been proposed to estimate the state of charge (SOC) of lithium-ion batteries. In automotive applications, the algorithms are subjected to changing load profiles, requiring investigations into their general performance under various working conditions. In this study, three different load patterns derived from a customized dynamic driving profile, a standard driving cycle, and a constant discharge are used for the experimental verification. Four selected algorithms including the Ampere-hour counting, the extended Kalman filter, the particle filter, and the recursive least square filter are implemented. Their performance in terms of accuracy and robustness are compared. In addition, the load profile is analyzed in the frequency domain. The results show that the filter performance is dependent on the current patterns and can be correlated to the frequency spectrum of the load profile.

Particle filter

State of charge

Recursive least square

Load condition

Electric vehicles

Extended Kalman filter


Shi Li

RWTH Aachen University

Changfu Zou

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Mirco Küpper

FEV Group GmbH

Stefan Pischinger

RWTH Aachen University

Energy Procedia

18766102 (ISSN)

Vol. 158 2806-2811

10th International Conference on Applied Energy, ICAE 2018
Hong Kong, China,




Annan elektroteknik och elektronik



Mer information

Senast uppdaterat