A Review of Remaining Useful Life Prediction for Energy Storage Components Based on Stochastic Filtering Methods

Lithium-ion batteries are a green and environmental energy storage component, which have become the first choice for energy storage due to their high energy density and good cycling performance. Lithium-ion batteries will experience an irreversible process during the charge and discharge cycles, which can cause continuous decay of battery capacity and eventually lead to battery failure. Accurate remaining useful life (RUL) prediction technology is important for the safe use and maintenance of energy storage components. This paper reviews the progress of domestic and international research on RUL prediction methods for energy storage components. Firstly, the failure mechanism of energy storage components is clarified, and then, RUL prediction method of the energy storage components represented by lithium-ion batteries are summarized. Next, the application of the data-model fusion-based method based on kalman filter and particle filter to RUL prediction of lithium-ion batteries are analyzed. The problems faced by RUL prediction of the energy storage components and the future research outlook are discussed.

Automated summary

This paper reviews the progress of domestic and international research on RUL prediction methods for energy storage components, with a focus on lithium-ion batteries. The failure mechanism of energy storage components is clarified, and RUL prediction methods are summarized. The application of data-model fusion-based methods to RUL prediction of lithium-ion batteries is discussed, along with the challenges and future research outlook.