Next Article in Journal
Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting
Next Article in Special Issue
Field Synergy Analysis and Optimization of the Thermal Behavior of Lithium Ion Battery Packs
Previous Article in Journal
Experimental Analysis and Full Prediction Model of a 5-DOF Motorized Spindle
Previous Article in Special Issue
Real-Time Velocity Optimization to Minimize Energy Use in Passenger Vehicles
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Energies 2017, 10(1), 76; doi:10.3390/en10010076

Detection of Internal Short Circuit in Lithium Ion Battery Using Model-Based Switching Model Method

1
Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 37673, Korea
2
Department of Creative IT Excellence Engineering and Future IT Innovation Laboratory, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 37673, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Kwok Tong Chau
Received: 22 November 2016 / Revised: 20 December 2016 / Accepted: 3 January 2017 / Published: 10 January 2017
(This article belongs to the Collection Electric and Hybrid Vehicles Collection)
View Full-Text   |   Download PDF [11424 KB, uploaded 10 January 2017]   |  

Abstract

Early detection of an internal short circuit (ISCr) in a Li-ion battery can prevent it from undergoing thermal runaway, and thereby ensure battery safety. In this paper, a model-based switching model method (SMM) is proposed to detect the ISCr in the Li-ion battery. The SMM updates the model of the Li-ion battery with ISCr to improve the accuracy of ISCr resistance R I S C f estimates. The open circuit voltage (OCV) and the state of charge (SOC) are estimated by applying the equivalent circuit model, and by using the recursive least squares algorithm and the relation between OCV and SOC. As a fault index, the R I S C f is estimated from the estimated OCVs and SOCs to detect the ISCr, and used to update the model; this process yields accurate estimates of OCV and R I S C f . Then the next R I S C f is estimated and used to update the model iteratively. Simulation data from a MATLAB/Simulink model and experimental data verify that this algorithm shows high accuracy of R I S C f estimates to detect the ISCr, thereby helping the battery management system to fulfill early detection of the ISCr. View Full-Text
Keywords: internal short circuit resistance; model updating method; battery safety internal short circuit resistance; model updating method; battery safety
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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Seo, M.; Goh, T.; Park, M.; Koo, G.; Kim, S.W. Detection of Internal Short Circuit in Lithium Ion Battery Using Model-Based Switching Model Method. Energies 2017, 10, 76.

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