Expert deep learning techniques for remaining useful life prediction of diverse energy storage Systems: Recent Advances, execution Features, issues and future outlooks

Abstract

A viable way to reduce carbon emissions and achieve sustainable development goals (SDGs) is through reliable and sustainable transportation, specifically through the utilization of electric vehicles (EVs) and integrated energy storage systems (ESSs). The operation and performance efficiency of EVs are based on accurate prediction of the remaining useful life (RUL), which improves the reliability, robustness, efficiency, and longevity of different ESSs like lithium-ion battery (LIB), supercapacitor (SC), and fuel cell (FC) in EV applications. Nonetheless, the RUL prediction of the LIB, SC and FC is considered difficult due to factors associated with capacity degradation and variable environmental conditions. In recent times, enormous attention to conduct the RUL prediction has been attained by expert deep learning (DL) techniques due to their supremacy with high volumes of data, fast computational performance, and large storage capacity. However, the DL techniques implemented for RUL prediction with ESS in EV applications are inadequate. Therefore, a novel review paper is presented which comprehensively reviews DL-based RUL prediction techniques for LIB, SC, and FC, encompassing DL models, their strength, weakness, accuracy, and research gaps. The objective of the presented review work is to demonstrate the superiority of the DL techniques for RUL prediction. Firstly, the review work examines different DL methods applied to conduct the RUL prediction of the LIB, SC and FC. Furthermore, the RUL prediction results with different factors like model implementation, critical hyperparameters, and data features/samples are analyzed. The review also outlines various execution features related to experiments, data, model features, and computational performance. Additionally, the current issues with the DL techniques for RUL prediction are identified. Lastly, future trends and outlooks are listed that would pave the way toward developing an accurate RUL prediction framework for ESSs technologies in reliable EV technology.