A Novel Construction Method and Prediction Framework of Periodic Time Series: Application to State of Health Prediction of Lithium-Ion Batteries

Due to the time property of natural phenomena and human activities, time series are very common in our lives. The analysis and study of time series can help us to better understand the world, predict the future and make scientific decisions. Focusing on time series prediction, in this paper we propose a method of constructing non-periodic time series into periodic time series and design a framework for time series prediction based on the constructed periodic time series. The proposed construction method and prediction framework for the periodic time series are then applied to predict the state of health (SOH) of lithium-ion (Li-ion) batteries. The effectiveness of the proposed approach is verified and evaluated on publicly available datasets from the National Aeronautics and Space Administration (NASA), Ames Prognostics Center of Excellence (PCoE), and Center for Advanced Life Cycle Engineering (CALCE) of University of Maryland. The experimental results show that the early SOH prediction of Li-ion batteries can be improved by at least one order of magnitude on both the NASA and CALCE battery datasets when using the method proposed in this paper.