Next Article in Journal
Dissemination of Solar Water Heaters in Taiwan: The Case of Remote Islands
Next Article in Special Issue
Optimal Conditions for Fast Charging and Long Cycling Stability of Silicon Microwire Anodes for Lithium Ion Batteries, and Comparison with the Performance of Other Si Anode Concepts
Previous Article in Journal
Assessment of the Economic and Environmental Impact of Double Glazed Fa├žade Ventilation Systems in Mediterranean Climates
Previous Article in Special Issue
Electrostatic Self-Assembly of Fe3O4 Nanoparticles on Graphene Oxides for High Capacity Lithium-Ion Battery Anodes
Energies 2013, 6(10), 5088-5100; doi:10.3390/en6105088
Article

Comparison Study on the Battery SoC Estimation with EKF and UKF Algorithms

* ,
,
 and
Received: 22 June 2013 / Revised: 21 August 2013 / Accepted: 24 September 2013 / Published: 30 September 2013
(This article belongs to the Special Issue Li-ion Batteries and Energy Storage Devices)
Download PDF [384 KB, uploaded 17 March 2015]

Abstract

The battery state of charge (SoC), whose estimation is one of the basic functions of battery management system (BMS), is a vital input parameter in the energy management and power distribution control of electric vehicles (EVs). In this paper, two methods based on an extended Kalman filter (EKF) and unscented Kalman filter (UKF), respectively, are proposed to estimate the SoC of a lithium-ion battery used in EVs. The lithium-ion battery is modeled with the Thevenin model and the model parameters are identified based on experimental data and validated with the Beijing Driving Cycle. Then space equations used for SoC estimation are established. The SoC estimation results with EKF and UKF are compared in aspects of accuracy and convergence. It is concluded that the two algorithms both perform well, while the UKF algorithm is much better with a faster convergence ability and a higher accuracy.
Keywords: electric vehicles; dynamic modeling; SoC estimation; extended Kalman filter; unscented Kalman filter electric vehicles; dynamic modeling; SoC estimation; extended Kalman filter; unscented Kalman filter
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote
MDPI and ACS Style

He, H.; Qin, H.; Sun, X.; Shui, Y. Comparison Study on the Battery SoC Estimation with EKF and UKF Algorithms. Energies 2013, 6, 5088-5100.

View more citation formats

Article Metrics

Comments

Citing Articles

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert