An improved model for remaining useful life prediction on capacity degradation and regeneration of lithium-ion battery

Abstract

The regeneration phenomena of the lithium-ion battery are widely existed in reality but rarely studied due to the gap between experiment conditions and practical working conditions. In this paper, the capacity regeneration phenomena are considered during the degradation process of batteries. An improved empirical model incorporating both rest time and discharge cycles for remaining useful life (RUL) prediction is proposed. The degradation process and regeneration process have been described by different components and integrated to formulate the whole model. The dual estimation framework is employed to decouple the states and parameters during the degradation and regeneration process. The datasets from NASA Prognostics Center of Excellence (PCoE) have been adopted for model validation. The proposed model is compared with other empirical model and also different estimation methods. The results are satisfactory, and demonstrate the capability of the proposed model for the RUL prediction of Lithium-ion battery.