Next Article in Journal
Optimal Operation of Microgrids Considering Auto-Configuration Function Using Multiagent System
Previous Article in Journal
An Improved Commutation Prediction Algorithm to Mitigate Commutation Failure in High Voltage Direct Current
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Energies 2017, 10(10), 1486; https://doi.org/10.3390/en10101486

Improved State of Charge Estimation for High Power Lithium Ion Batteries Considering Current Dependence of Internal Resistance

1
College of Automotive Engineering, Chongqing University, Chongqing 40044, China
2
China Chang’an Automotive Engineering Institute, Chongqing 401120, China
3
A123 Systems, LLC., Livonia, MI 48377, USA
*
Author to whom correspondence should be addressed.
Received: 6 August 2017 / Revised: 13 September 2017 / Accepted: 19 September 2017 / Published: 25 September 2017
(This article belongs to the Section Energy Storage and Application)
View Full-Text   |   Download PDF [2502 KB, uploaded 26 September 2017]   |  

Abstract

For high power Li-ion batteries, an important approach to improve the accuracy of modeling and algorithm development is to consider the current dependence of internal resistance, especially for large current applications in mild/median hybrid electric vehicles (MHEV). For the first time, the work has experimentally captured the decrease of internal resistance at an increasing current of up to the C-rate of 25 and developed an equivalent circuit model (ECM) with current dependent parameters. The model is integrated to extended Kalman filter (EKF) to improve SOC estimation, which is validated by experimental data collected in dynamic stress testing (DST). Results show that EKF with current dependent parameters is capable of estimating SOC with a higher accuracy when it is compared to EKF without current dependent parameters. View Full-Text
Keywords: Li-ion battery modeling; current dependence; state of charge estimation; extended Kalman filter; battery management system Li-ion battery modeling; current dependence; state of charge estimation; extended Kalman filter; battery management system
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

Wu, C.; Fu, R.; Xu, Z.; Chen, Y. Improved State of Charge Estimation for High Power Lithium Ion Batteries Considering Current Dependence of Internal Resistance. Energies 2017, 10, 1486.

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