Model-Based Online Detection, Quantification and Localization of Internal Short Circuits in Lithium-Ion Pouch Cells via an Unscented Kalman Filter

Abstract

Internal short circuits (ISCs) are a leading precursor of thermal runaway in lithium-ion batteries, yet they are difficult to observe directly because their early electrical signature is small and ambiguous and their thermal signature is spatially distributed. This paper presents a model-based observer that simultaneously detects an ISC, quantifies its severity, and localizes it on the surface of a pouch cell using only the applied current, the terminal voltage, and a small array of surface temperature sensors. A lumped electro-thermal model couples a third-order equivalent-circuit model (ECM) of the cell to a two-dimensional lumped-parameter thermal network (LPTN); the ISC is parameterized by a shunt conductance G and a planar location (x, y), and is the source of an additional ohmic heat term injected locally into the thermal field. These three fault parameters are appended to the dynamic state and estimated online with a single joint unscented Kalman filter (UKF). A location-observability gate suppresses spurious position updates until the measured spatial thermal contrast is informative, and the detection instant is defined as the time at which the filter covariance converges to a stable value. The method is evaluated on a design of experiments (DoE) of subcritical shorts generated by a high-fidelity electrochemical–thermal plant, spanning three severities and nine locations. For strong and moderate shorts the observer recovers severity to within about 7% and location to within about 2 mm, converging in roughly 110 s; for the weakest short the severity is quantifiable only to order of magnitude and the thermal contrast is insufficient to localize, defining the detectability floor of the sensing arrangement.