| 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 |
| DOI |
https://doi.org/10.6113/TKPE.2022.27.4.316 |
| 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. |