Online internal short circuit detection for a large format lithium ion battery

Abstract

Early detection of an internal short circuit (ISC) in lithium ion batteries has become a crucial task for battery management, as ISC is believed to be the root cause of several large format lithium ion battery fire accidents. In this paper, a scheme of on-line detection of ISC is proposed, and the online ISC detection problem is addressed from a model parameterization and parameter estimation perspective. Using a 3D electrochemical-thermal-ISC coupled model, we explore the correlation between the measured voltage, current, and temperature data and the ISC status. It is identified that the abnormal depletion in the state-of-charge (SOC) and excessive heat generation associated with ISC affect the voltage and temperature responses, and that the correlation can be captured by a properly parameterized phenomenological model. The ISC detection is then recast as a parameter estimation problem, for which a model-based estimation algorithm is proposed and evaluated. It is shown that the estimation algorithm can track the parameter variations in real-time, thereby making it feasible to track ISC incubation status or to detect instantaneously triggered ISC. Moreover, it is observed that the recorded temperature profile is not affected by the location where the ISC occurs, due to the oval shape of the temperature distribution caused by anisotropic heat conduction of the battery core. Therefore, the proposed algorithm can detect the ISC, regardless of its physical location within the battery.