An Integrated Method of the Future Capacity and RUL Prediction for Lithium-Ion Battery Pack

Accurate prediction of remaining useful life (RUL) is of critical significance to the safety and reliability of lithium-ion batteries, which can offer efficient early warning signals for failure. Due to the complicated aging mechanism and realistic noise operation environment, direct predicting RUL with the measured data recorded in practice is challenging. In this work, a novel hybrid approach to forecasting battery future capacity and RUL is proposed by combining the improved variational modal decomposition (VMD), particle filter (PF) and gaussian process regression (GPR). The VMD algorithm is employed to decompose the recorded battery capacity data into an aging trend sequence and several residual sequences, where the number of modal layers is produced by the proposed posterior feedback confidence (PFC) method. The prediction models of PF and GPR algorithm are then respectively established to predict the aging trend sequence and residual sequences. Future capacity and RUL prediction experiments for battery pack and battery cells are performed to verify the effectiveness of the proposed hybrid approach, and the compared experiment results demonstrate that the proposed approach offers wide generality and reduced errors.

Automated summary

This paper proposes a hybrid approach to predict the future capacity and remaining useful life (RUL) of batteries by combining improved variational modal decomposition (VMD), particle filter (PF), and Gaussian process regression (GPR). Experimental results demonstrate that the proposed approach offers wide generality and reduced errors.