Filtering techniques for lithium-ion battery state of health and remaining useful life prediction

Battery safety, longevity, and reliability are enhanced through accurate assessment of the state of health (SOH) and remaining useful life (RUL). Traditional research often relies on a single model framework to estimate SOH/RUL in lithium-ion batteries (LIBs). However, due to the complex internal mechanisms of LIBs and varying external conditions, a single model may not provide reliable predictions. Recently, increasing attention has been given to hybrid techniques that combine data-driven and model-based approaches with filtering methods such as Kalman filters (KF) and particle filters (PFs), given their accuracy and robustness across different environments. Still, relatively few studies focus on filtering-based assessments of SOH/RUL. This work provides a review of hybrid approaches integrated with KF and PF for SOH/RUL estimation. It also examines co-estimation methods and discusses applications in electric vehicles, while outlining key challenges and future research directions.