A novel thermal runaway warning method of lithium-ion batteries

Abstract

To improve the safety of electric vehicles and battery energy storage systems, early prediction of thermal runaway (TR) is of great significance. This work proposes a novel method for early warning and short-term prediction of the TR. To give warning of TR long time in advance, a variety of battery models are established to extract key features, such as Pauta feature and Shannon entropy of voltage deviation, and then local outlier factor algorithm is used for feature fusion to detect abnormal cells. For the short-term pre-diction, the predefined threshold and variation rates are used. By measuring the real-time signals, such as voltage and temperature, their variation rates are calculated, based on which TR can be predicted exactly. The real data including TR from an electric vehicle are used to verify the method that it can give a warning on TR long time before it happens up to 74 days. This is remarkable for providing replacement recommendations for abnormal cells. It can also predict the occurrence of TR 33 seconds in advance to ensure the safe use of batteries.