Mobile QR Code QR CODE : The Korean Institute of Power Electronics
Title Degradation Prediction and Analysis of Lithium-ion Battery using the S-ARIMA Model with Seasonality based on Time Series Models
Authors Seungwoo Kim ; Pyeong-Yeon Lee ; Sanguk Kwon ; Jonghoon Kim
Page pp.316-324
ISSN 1229-2214
Keywords ARMA (Auto Regressive Moving Average); Lithium-ion battery; S-ARIMA (Seasonal Auto Regressive Integrated Moving Average); SOH (State-of-Health)
Abstract This paper uses seasonal auto-regressive integrated moving average (S-ARIMA), which is efficient in seasonality between time-series models, to predict the degradation tendency for lithium-ion batteries and study a method for improving the predictive performance. The proposed method analyzes the degradation tendency and extracted factors through an electrical characteristic experiment of lithium-ion batteries, and verifies whether time-series data are suitable for the S-ARIMA model through several statistical analysis techniques. Finally, prediction of battery aging is performed through S-ARIMA, and performance of the model is verified through error comparison of predictions through mean absolute error.