Predictive Maintenance of Lithium-Ion Batteries: A Review on Remaining useful Life Prediction Models

The process of predicting Remaining Useful Life (RUL) for lithium-ion batteries provides an important safety assessment which leads to reduced maintenance expenses and better operational forecasting of battery systems used in portable devices and electric vehicles and renewable energy storage solutions. The operational process of the system suffers from battery degeneration because this process occurs through multiple stages which depend on different operational factors such as temperature and load profile and current operating conditions. The research paper presents a complete evaluation of all existing techniques which use machine learning and deep learning and statistical and physics-based methods and hybrid systems to predict lithium-ion battery Remaining Useful Life. The established methods enable users to monitor system performance through their uncertainty prediction abilities, but the methods fail to deliver precise results under changing environmental conditions. The datadriven machine learning and deep learning models, which include LSTM and transformerbased architectures, demonstrate superior predictive capabilities because they can recognize long-term battery aging patterns from their degradation data. The hybrid approaches lead to better results than the base performance of the models through their combination of CEEMDAN decomposition and incremental capacity analysis signal processing techniques. The review presents detailed information about current research topics which include physicsinformed machine learning and transfer learning and system interpretability and uncertainty quantification. The research identifies two major obstacles which need more study, while it emphasizes the need to create models which can explain their decisions and assess uncertainty that can function in actual Battery systems.