Abstract
Failures of lithium-ion batteries (LIBs) arise in both energetic and non-energetic ways. Both modes may occur due to numerous reasons such as cell manufacturing flaws, poor cell design (electrical or mechanical), external abuse of cells (thermal, mechanical, or electrical), defective protection electronics, chargers, etc. In this work, we present a machine learning (ML) study of the most severe energetic failure of the LIBs, which occurs due to the thermal runaway (TRA). In general, the TRA consists of a rapid self-heating of a cell sourced from exothermic (electro-) chemical reactions. During the TRA, the excessive heat has a detrimental influence on the electric characteristics and the state-of-charge (SOC) of the LIBs, which could lead to the accidental explosion of the battery. During the TRA, it is impossible to directly monitor the occurring events in practical operating conditions. However, the change of electric parameters during the TRA like-events could potentially indicate the existence of a failure, thus allowing to foresee the LIBs malfunction.
We propose two deep learning techniques to address the problem of predicting the TRA like-events based upon the temporal variation of electric parameters, the SOC and the thermal images in overheating conditions. First, a recurrent neural network (RNN) built upon the long-short-term memory (LSTM) scheme is used to estimate the time dependency of the SOC on the voltage, current and temperature of a cell. In the second method the objective is to predict the presence of the TRA in the LIBs subjected to internal/external heating directly from thermal images collected as sequences of video frames during the battery operation. For this purpose, a Convolutional LSTM (Conv-LSTM) network is constructed and trained to distinguish the recorded thermal images among two classes, specifically the presence or absence of the TRA like-events. Conv-LSTM is a recurrent neural network designed to extract information from sequential images in time series analysis, which combines the image processing capabilities of convolutional neural networks (CNN) and the temporal tracking potential of LSTM. The main advantage of the proposed techniques is that they do not require the physical modeling of the internal battery chemistry, which is cumbersome in the TRA scenario, but they rely only on the experimental/ modeling data and the collected thermal images,