Accurate prediction of the remaining useful life (RUL) is a key function for ensuring safety and stability lithium-ion batteries. To solve capacity regeneration model adaptability under different working conditions, hybrid RUL based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) bi-directional gated recurrent unit (BiGRU) proposed. CEEMDAN used to divide into in...

Abstract The remaining useful life (RUL) prediction is of significance to the health management bearings. Recently, deep learning has been widely investigated for bearing RUL due its great success in sequence learning. However, improvement accuracy existing algorithms heavily relies on feature engineering such as handcrafted generation and time–frequency transformation, which increase complexit...