Publication

Abstract : The work aims on determining the Remaining useful life (RUL) using a recurrent method. It is a deep learning method. A specific dataset is chosen and accuracy is measured considering various parameters. RUL is has a major role in BMS (Battery Management System). The prediction of accurate RUL is very important and there are various methods employed in this prediction. The State of Health (SOH) estimation becomes tedious task due to the non-uniform behavior of battery degradation. SOH of battery is one key parameter to decide remaining useful life of a battery. This also applies to batteries and it improves RUL estimation. This paper is aimed to bring out the metrics related to RUL estimation for 2 machine learning methods and Independently recurrent neural network(IndRNN) for a chosen dataset.