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http://dx.doi.org/10.6113/TKPE.2022.27.4.316

Degradation Prediction and Analysis of Lithium-ion Battery using the S-ARIMA Model with Seasonality based on Time Series Models  

Kim, Seungwoo (Department of Electrical Engineering, Chungnam National University)
Lee, Pyeong-Yeon (Department of Electrical Engineering, Chungnam National University)
Kwon, Sanguk (Department of Electrical Engineering, Chungnam National University)
Kim, Jonghoon (Dept. of Electrical Engineering, Chungnam National University)
Publication Information
The Transactions of the Korean Institute of Power Electronics / v.27, no.4, 2022 , pp. 316-324 More about this Journal
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.
Keywords
ARMA (Auto Regressive Moving Average); Lithium-ion battery; S-ARIMA (Seasonal Auto Regressive Integrated Moving Average); SOH (State-of-Health);
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