A review of hybrid methods based remaining useful life prediction framework and SWOT analysis for energy storage systems in electric vehicle application

The diverse energy storage systems (ESSs) in electric vehicle (EV) applications are one practical approach to accomplishing the sustainable development goals (SDGs) and reducing carbon emissions. The operational performance of EVs can be improved with accurate remaining useful life (RUL) prediction of energy storage devices (ESSs) such as lithium-ion batteries (LIBs), supercapacitors (SCs), and fuel cells (FCs). However, the degradation of LIB, SC, and FC during charging and discharging occurs, and therefore, it is necessary to carry out accurate RUL prediction. Generally, the RUL prediction of the LIB, SC, and FC is conducted using a single model structure. Nonetheless, a single model structure for the RUL prediction may not yield accurate outcomes due to its inability to analyze the complex degradation mechanisms and variable external factors associated with LIB, SC, and FC. Therefore, the novelty of this review work focuses on studying the hybrid methods utilized for predicting the remaining useful life (RUL) of energy storage devices such as LIB, SC, and FC in EV applications The objective of the presented review work is to demonstrate the superiority of the state-of-the-art hybrid methods compared with a single model structure framework for RUL prediction with LIB, SC, and FC. At first, the presented review work examines the previously published review literature with RUL prediction of LIB, SC, and FC, depicting their research fields and related research gaps. Secondly, a brief discussion is conducted on the failure models and degradation mechanisms of LIB, SC, and FC technologies. Furthermore, the hybrid methods employed, comprising the hybrid model/data-driven methods specifically with particle filters (PF), Kalman filters (KF), optimization techniques, and other stochastic model/filter techniques, are reviewed, focusing on the implemented methods, outcomes, advantages, and disadvantages. The strength, weakness, opportunity, and threat (SWOT) analysis for LIB, SC, and FC technology is conducted to deliver a better perspective on the selection of appropriate ESS for different engineering applications. Additionally, the review outlines the current issues and limitations associated with hybrid methods for RUL prediction in LIB, SC, and FC. Lastly, the work also discusses future outlooks and provides suggestions for developing an accurate RUL prediction framework for various ESS technologies in EV applications.