Next Article in Journal
A Floating Platform with Embedded Wave Energy Harvesting Arrays in Regular and Irregular Seas
Next Article in Special Issue
A High-Frequency Isolation (HFI) Charging DC Port Combining a Front-End Three-Level Converter with a Back-End LLC Resonant Converter
Previous Article in Journal
Design Improvisation for Reduced Harmonic Distortion in a Flux Pump-Integrated HTS Generator
Previous Article in Special Issue
Online Lithium-Ion Battery Internal Resistance Measurement Application in State-of-Charge Estimation Using the Extended Kalman Filter
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Energies 2017, 10(9), 1345; https://doi.org/10.3390/en10091345

An Adaptive Square Root Unscented Kalman Filter Approach for State of Charge Estimation of Lithium-Ion Batteries

School of Control Science and Engineering, Shandong University, Jinan 250061, China
*
Authors to whom correspondence should be addressed.
Received: 15 July 2017 / Revised: 18 August 2017 / Accepted: 1 September 2017 / Published: 6 September 2017
(This article belongs to the Collection Electric and Hybrid Vehicles Collection)
Full-Text   |   PDF [3093 KB, uploaded 6 September 2017]   |  

Abstract

An accurate state of charge (SOC) estimation is of great importance for the battery management systems of electric vehicles. To improve the accuracy and robustness of SOC estimation, lithium-ion battery SOC is estimated using an adaptive square root unscented Kalman filter (ASRUKF) method. The square roots of the variance matrices of the SOC and noise can be calculated directly by the ASRUKF algorithm, which ensures the symmetry and nonnegative definiteness of the matrices. The process values and measurement noise covariance can be adaptively adjusted, which greatly improves the accuracy, stability, and self-adaptability of the filter. The effectiveness of the proposed method has been verified through experiments under different operating conditions. The obtained results were compared with those of extended Kalman filter (EKF) and unscented Kalman filter (UKF) , which indicates that the ASRUKF method provides better accuracy, robustness and convergence in the estimation of battery SOC for electric vehicles. The proposed method has a mean SOC estimation error of 0.5% and a maximum SOC estimation error of 0.8%. These errors are lower than those of other methods. View Full-Text
Keywords: extended Kalman filter (EKF); adaptive square root unscented Kalman filter (ASRUKF); state of charge (SOC); lithium-ion battery extended Kalman filter (EKF); adaptive square root unscented Kalman filter (ASRUKF); state of charge (SOC); lithium-ion battery
Figures

Figure 1

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. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Liu, S.; Cui, N.; Zhang, C. An Adaptive Square Root Unscented Kalman Filter Approach for State of Charge Estimation of Lithium-Ion Batteries. Energies 2017, 10, 1345.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

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