Interacting Multiple-Model Method for Fault Detection and Short Resistance Estimation in Parallel Connected Lithium-Ion Batteries

Abstract Detecting internal short circuits (ISCs) in a single cell connected in parallel with others is challenging because unmeasured internal currents can obscure measurable indicators such as charge loss and voltage drop from the faulty cell. In this work, we propose a new method for detecting ISCs based on the interacting multiple model (IMM) estimation technique, which can provide a probability for the occurrence of an ISC and simultaneously estimate the short-circuit resistance, indicating the severity of the ISC. The IMM relies on dynamic electrothermal models of parallel cells (nP), both for the healthy mode and short-circuit mode. The IMM technique is combined with unscented Kalman filters (UKFs) to detect internal short circuits and estimate the short-circuit resistance across various synthetic data sets that are corrupted by Gaussian noise for different values of ISC resistance. Fifty short-circuit scenarios were simulated in which one cell in a 46 P cell group underwent an ISC during a drive cycle. The short-circuit resistances ranged from 0.5 to 100 Ω, tested at ten different states of charge (SOCs). Our simulation outputs included busbar voltage, input current, and cell temperatures, which were then corrupted by Gaussian noise. Our IMM successfully detected and estimated the ISC in all fifty cases, with temperature rise remaining below 6 °C before detection, well before the onset of thermal runaway conditions.