A Spatio-Temporal Inference System for Abnormality Detection and Localization of Battery Systems

Abstract

In this research, a spatio-temporal inference system is proposed to detect and locate thermal abnormalities of battery systems. The proposed spatio-temporal inference system consists of three modules: spatio-temporal processing module, abnormality inference module, and spatial inference module. Based on the distributed temperatures on the battery system, the monitoring statistic can be developed in the spatio-temporal processing module. The abnormality inference module is constructed to detect the abnormality based on the derived statistic index. Then, the spatial Bayes model is designed to estimate the abnormality location. The Bayes risk analysis indicates that the proposed method has a bounded error. Experiments on a lithium-ion (Li-ion) battery cell and a battery pack demonstrate that the proposed spatio-temporal inference system can detect and locate the internal short circuit (ISC) fault before it develops into a thermal runaway.