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

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
Energies 2017, 10(1), 76; https://doi.org/10.3390/en10010076
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)
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
Show Figures

Figure 1

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. https://doi.org/10.3390/en10010076

AMA Style

Seo M, Goh T, Park M, Koo G, Kim SW. Detection of Internal Short Circuit in Lithium Ion Battery Using Model-Based Switching Model Method. Energies. 2017; 10(1):76. https://doi.org/10.3390/en10010076

Chicago/Turabian Style

Seo, Minhwan, Taedong Goh, Minjun Park, Gyogwon Koo, and Sang W. Kim 2017. "Detection of Internal Short Circuit in Lithium Ion Battery Using Model-Based Switching Model Method" Energies 10, no. 1: 76. https://doi.org/10.3390/en10010076

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

Article Access Map by Country/Region

1
Back to TopTop