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A Data Preprocessing Framework for Improving Estimation Accuracy of Battery Remaining Time in Mobile Smart Devices

모바일 스마트 장치 배터리의 잔여 시간 예측 향상을 위한 데이터 전처리 프레임워크

  • Tak, Sungwoo (School of Electrical and Computer Engineering, Pusan National University)
  • Received : 2020.01.04
  • Accepted : 2020.02.25
  • Published : 2020.04.30

Abstract

When general statistical regression methods are applied to predict the battery remaining time of a mobile smart device, they yielded the poor accuracy of estimating battery remaining time as the deviations of battery usage time per battery level became larger. In order to improve the estimation accuracy of general statistical regression methods, a preprocessing task is required to refine the measured raw data with large deviations of battery usage time per battery level. In this paper, we propose a data preprocessing framework that preprocesses raw measured battery consumption data and converts them into refined battery consumption data. The numerical results obtained by experimenting the proposed data preprocessing framework confirmed that it yielded good performance in terms of accuracy of estimating battery remaining time under general statistical regression methods for given refined battery consumption data.

모바일 스마트 장치 배터리의 잔여 시간을 예측하기 위해 범용 통계적 회귀 기법을 적용한 경우, 배터리 잔량별 배터리 사용 시간의 편차가 커질수록 범용 통계적 회귀 기법의 예측 정확도가 낮아진다. 따라서 범용 통계적 회귀 기법의 예측 정확도를 향상시키기 위해서는 배터리 잔량별 배터리 사용 시간의 편차가 큰 원 측정 데이터를 가공 처리하여 정제된 데이터로 변환시키는 작업이 필요하다. 이에 본 논문에서는 원 측정 데이터를 정제된 데이터로 가공 처리하는 데이터 전처리 프레임워크를 제안하였다. 제안한 프레임워크를 통해 가공 처리하여 정제된 데이터를 범용 통계적 회귀 기법에 적용한 결과, 범용 통계적 회귀 기법의 예측 정확도가 향상됨을 확인하였다.

Keywords

Acknowledgement

This work was supported by a 2-Year Research Grant of Pusan National University

References

  1. Y. Xing, E. Ma, K-L. Tsui, and M. Pecht, "An ensemble for predicting the remaining useful performance of lithium-ion batteries," Microelectronics Reliability, vol. 53, no. 6, pp.811-820, June 2013. https://doi.org/10.1016/j.microrel.2012.12.003
  2. W. Wang, X. Wang, C. Xiang, C. Wei, and Y. Zhao, "Unscented kalman filter-based battery SOC estimation and peak power prediction method for power distribution of hybrid electric vehicles," IEEE Access, vol. 6, pp. 35957-35965, June 2018. https://doi.org/10.1109/access.2018.2850743
  3. W. Yan, B. Zhang, G. Zhao, S. Tang, G. Niu, and X. Wang, "A battery management system with a lebesgue-sampling-based extended kalman filter," IEEE Transactions on Industrial Electronics, vol. 66, no. 4, pp. 3227-3236, April 2019. https://doi.org/10.1109/TIE.2018.2842782
  4. Y. Zhang, R. Xiong, H. He, and Z. Liu, "A LSTM-RNN method for the lithium-ion battery remaining useful life prediction," Proceeding of 2017 Prognostics and System Health Management Conference, China: Harbin, pp.811-820, July 2017.
  5. R. Richardson, M. Osborne, and D. Howey, "Gaussian process regression for forecasting battery state of health," Journal of Power Sources, vol. 357, pp. 209-219, July 2017. https://doi.org/10.1016/j.jpowsour.2017.05.004
  6. T. Mesbahi, F. Khenfri, N. Rizoug, K. Chaaban, P. Bartholomeus, and P. Moigne, "Dynamical modeling of Li-ion batteries for electric vehicle applications based on hybrid Particle Swarm-Nelder-Mead (PSO-NM) optimization algorithm," Electric Power Systems Research, vol. 131, pp. 195-204, Feb. 2016. https://doi.org/10.1016/j.epsr.2015.10.018
  7. Y. Zhang, R. Xiong, H. He, and M. Pecht, "Lithium-ion battery reamining useful life prediction with box-cox transformation and Monte Carlo simulation," IEEE Transactions on Industrial Electronics, vol. 66, no. 2, pp.1585-1597, Feb. 2019. https://doi.org/10.1109/TIE.2018.2808918
  8. J. Wiley, R Deep learning essentials, 1th ed. Birmingham, Packt Publishing, 2016.
  9. L. Martino, D. Luengo, J. Miguez, Independent random sampling methods, 1th ed. Switzerland, Springer, 2018.
  10. A. Saksonov, "Method to derive energy profiles for android platform," Master thesis, University of Oldenburg, Oldenburg, Germany, 2014.
  11. M. Kuhn and K. Johnson, Applied predictive modeling, 1st ed. New York, Springer, 2013.