[논문]시계열 모델 기반의 계절성에 특화된 S-ARIMA 모델을 사용한 리튬이온 배터리의 노화 예측 및 분석

김승우; 이평연; 권상욱; 김종훈

doi:10.6113/tkpe.2022.27.4.316

시계열 모델 기반의 계절성에 특화된 S-ARIMA 모델을 사용한 리튬이온 배터리의 노화 예측 및 분석
Degradation Prediction and Analysis of Lithium-ion Battery using the S-ARIMA Model with Seasonality based on Time Series Models 원문보기

전력전자학회 논문지 = The Transactions of the Korean Institute of Power Electronics, v.27 no.4, 2022년, pp.316 - 324

김승우 (Department of Electrical Engineering, Chungnam National University) , 이평연 (Department of Electrical Engineering, Chungnam National University) , 권상욱 (Department of Electrical Engineering, Chungnam National University) , 김종훈 (Dept. of Electrical Engineering, Chungnam National University)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (17)

그림 Fig. 1. Advantages of ARMA-based models compared to machine learning models
그림 Fig. 2. Electrical characteristic experimental conditions and experimental processes
그림 Fig. 3. Experimental profiles of capacity measurement test for lithium-ion batteries
그림 Fig. 4. Experimental profiles of aging test for lithiumion batteries
표 TABLE I SPECIFICATIONS FOR INR-18650 25R BATTERY
표 TABLE II INITIAL CHARGING AND DISCHARGING CAPACITIES OF THE THREE CELLS TESTED
그림 Fig. 5. The method of extracting discharge capacity and resistance for lithium-ion batteries in the aging experiment.
그림 Fig. 6. Graph of capacity decrease for lithiumion battery over 300 cycles
그림 Fig. 7. Graph of resistance increase for lithiumion battery over 300 cycles
그림 Fig. 8. Decrease in lithium-ion battery capacity caused by the aging characteristic experiment
그림 Fig. 9. S-ARIMA model process
그림 Fig. 10. ACF and PACF graphs for auto-correlation analysis of time series data
표 TABLE III CONDITIONS FOR SELECTING VARIABLES OF ACF
표 TABLE IV CONDITIONS FOR SELECTING VARIABLES OF ACF
표 TABLE V RESULT OF AKAIKE'S INFORMATION CRITERION FOR TIME SERIES DATA OF BATTERIES
그림 Fig. 11. S-ARIMA graph based on learning data according to cycles (a)100, (b)150, (c)200, (d)250
표 TABLE VI COMPARISON OF ERRORS IN THE S-ARIMA PREDICTION MODEL BY TRAINING DATA

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