A Review of Modern Machine Learning Techniques in the Prediction of Remaining Useful Life of Lithium-Ion Batteries

The intense increase in air pollution caused by vehicular emissions is one of the main causes of changing weather patterns and deteriorating health conditions. Furthermore, renewable energy sources, such as solar, wind, and biofuels, suffer from weather and supply chain-related uncertainties. The electric vehicles' powered energy, stored in a battery, offers an attractive option to overcome emissions and uncertainties to a certain extent. The development and implementation of cutting-edge electric vehicles (EVs) with long driving ranges, safety, and higher reliability have been identified as critical to decarbonizing the transportation sector. Nonetheless, capacity deteriorating with time and usage, environmental degradation factors, and end-of-life repurposing pose significant challenges to the usage of lithium-ion batteries. In this aspect, determining a battery's remaining usable life (RUL) establishes its efficacy. It also aids in the testing and development of various EV upgrades by identifying factors that will increase and improve their efficiency. Several nonlinear and complicated parameters are involved in the process. Machine learning (ML) methodologies have proven to be a promising tool for optimizing and modeling engineering challenges in this domain (non-linearity and complexity). In contrast to the scalability and temporal limits of battery degeneration, ML techniques provide a non-invasive solution with excellent accuracy and minimal processing. Based on recent research, this study presents an objective and comprehensive evaluation of these challenges. RUL estimations are explained in detail, including examples of its approach and applicability. Furthermore, many ML techniques for RUL evaluation are thoroughly and individually studied. Finally, an application-focused overview is offered, emphasizing the advantages in terms of efficiency and accuracy.