An Overview of Remaining Useful Life Prediction of Battery Using Deep Learning and Ensemble Learning Algorithms on Data-Dependent Models

There has been expeditious development and significant advancements accomplished in the electrified transportation system recently. The primary core component meant for power backup is a lithium-ion battery. One of the keys to assuring the vehicle?s safety and dependability is an accurate remaining useful life (RUL) forecast. Hence, the exact prediction of RUL plays a vital part in the management of battery conditions. However, because of its complex working characteristics and intricate deterioration mechanism inside the battery, predicting battery life by evaluating exterior factors is exceedingly difficult. As a result, developing improved battery health management technology successfully is a massive effort. Because of the complexity of ageing mechanisms, a single model is unable to describe the complex deterioration mechanisms. As a result, this paper review is organised into three sections. First is to study about the battery degradation mechanism, the second is about battery data collections using mercantile and openly accessible Li-ion battery data sets and third is the estimation of battery RUL. The important performance parameters of distinct RUL forecast and estimation are categorised, analysed and reviewed. In the end, a brief explanation is given of the various performance error indices. This article classifies and summarises the RUL prediction by data-dependent models using machine learning (ML), deep learning (DL) and ensemble learning (EL) algorithms suggested in a last few years. The goal of this work in this context is to present an overview of all recent advancements in RUL prediction utilising all three data-driven models. This article is also followed by a categorisation of several types of ML, DL and EL algorithms for RUL prediction. Finally, this review-based study includes the pros and cons of the models.