Li-Ion Battery Temperature Forecasting Method: Case-Study

Abstract

Monitoring and managing battery health is crucial for enhancing performance and lowering running expenses for electronic devices. This paper covers the Deep-learning-enabled temperature forecasting for Li- ion batteries, where they are tested independently. This research presents time series forecasting approaches to predict the temperature of the battery packs. In the proposed model, a Long Short-Term Memory (LSTM) and autoregressive integrated moving average (ARIMA) for predicting the battery temperature and beware of probable future temperatures beforehand to minimize the chances of overcharging and prevent the battery from crossing the threshold value above which battery's health characteristics might get hampered. The growing popularity of data-driven battery prognostics methods shows that ARIMA and LSTM are even when there aren't many prior details available about the batteries. Have a unique dataset of 34 lithium-ion battery packs for this challenge. In one way, the results imply that the existing ARIMA techniques offer interpreting data at various batteries. Having said that, LSTM model outcome recommend that the developed Univariate and Multivariate LSTM model provides finer prediction accuracy in the existence of greater diversification in data for one battery. Thus, try to generalize one forecasting model for each battery type depending on the model's performance.