Remaining Useful Life Prediction of Lithium-Ion Batteries Using Monotone Decomposition

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is vital for efficient equipment health management. Throughout the aging process, the battery capacity exhibits nonlinear behavior, with intermittent capacity regeneration phenomena causing sudden increments between consecutive cycles, posing challenges for modeling and prediction. Despite the frequent use of empirical mode decomposition (EMD) to decompose capacity series, most EMD-based RUL prediction methods encounter limitations including end effects, information leakage issues, and a lack of uncertainty quantification. To address these challenges, we introduce a novel RUL prediction framework, MonoD-GPR-DeepAR, featuring a unique data decomposition algorithm, monotone decomposition (MonoD). MonoD alleviates end effects by decoupling the original capacity signal into a smooth, decreasing trend and a fluctuant capacity regeneration term. Gaussian process regression (GPR) and deep autoregressive (DeepAR) models are then applied to the subseries for prediction, including uncertainty intervals. Validation using simulations and three real lithium-ion battery datasets demonstrates MonoD’s superior performance in capturing the authentic aging trajectory characteristics. Compared to alternative methods, the MonoD-GPR-DeepAR model shows its effectiveness in addressing complexities introduced by capacity regeneration phenomena in lithium-ion battery RUL prediction.