Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

“…These methods extract effective feature information and construct the degradation model to predict RUL. These methods are able to describe degradation-inherent relationships and trends based on data [10]. Therefore, data-driven methods have become the focus of RUL prediction in the world [11].…”

A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation

“…Liu et al [29] use the PF with an autoregressive time series degradation model for RUL estimation. Some recent modifications to the PF algorithm are also exploited for battery state estimation, like regularized auxiliary PF in [30], unscented PF in [31] and GaussHermite PF in [32].…”

EMA remaining useful life prediction with weighted bagging GPR algorithm

“…Actually, if a fault is required to be detected and isolated when it occurred, its RUL should be predicted using an effective and timely method [21]. RUL prediction also ensures that some measures can be taken in case of the whole aircraft running to failures [22,23,24,25]. Furthermore, RUL prediction allows active replacement, planned maintenance procedures, and enhanced decisions of fleet deploy [15,26].…”