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

Abstract

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 ISC 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 ISC 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 ISC f . Then the next R ISC 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 ISC f estimates to detect the ISCr, thereby helping the battery management system to fulfill early detection of the ISCr.

Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell

“…The I L is divided into two currents which are the self-discharge current I 1L flowing through the R ISCr and residual current I 2L (Equation (4)), and V t, f is the terminal voltage of the faulted cell. The faulted cell model is described in Equations (5) and (6) [33,38]. To represent the model of the battery pack with ISCr, the V t,p is obtained by adding the V t,n and the V t, f , as shown in Equation (7).…”

“…Using the self-discharge phenomenon [32,33], the R ISCr can be estimated with the estimated SOC f . To formulate Equation (16) for calculating the R ISCr , the Coulomb counting method is used with respect to the SOC f .…”

“…Therefore, the ISCr detection methods, which directly use the R ISCr as the fault index, have been introduced [29,[31][32][33]. With measurement data of the self-discharge current and the terminal voltage of the battery with ISCr in the particular experiment configuration, the R ISCr can be calculated correctly [29,31].…”

“…However, the accuracy of the R ISCr estimated from two different load current profiles was low, because the R ISCr in the equivalent circuit model was not used to estimate open circuit voltage (OCV) of the battery with ISCr. To overcome this error, the previously estimated R ISCr in the model was used to update the model iteratively and to estimate the OCV, and then the next estimated R ISCr (5∼50 Ω) became accurate enough to detect the ISCr [33].…”

Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

“…For external short-circuit detection, Chen et al [16] presented a two-layer model-based a fault diagnosis algorithm, which can give a precise model-based diagnosis. Seo et al [17] proposed a high accuracy modelbased switching model method (SMM) to detect the internal short circuit (ISCr) in the Li-ion battery, which helping the battery management system to fulfill early detection of the ISCr. Zhang et al [18] proposed a novel method is to perform online and real-time capacity fault diagnosis for a parallel-connected battery group (PCBG) and the fault simulation and validation results demonstrate that the proposed methods have good accuracy and reliability.…”