한국융합학회논문지 (Journal of the Korea Convergence Society)

  • 제11권6호
  • /
  • Pages.7-14
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  • 2020
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  • 2233-4890(pISSN)
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  • 2713-6353(eISSN)
한국융합학회 (Korea Convergence Society)
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확장 칼만 필터를 이용한 배터리 모니터링 시스템 개발

Development of Battery Monitoring System Using the Extended Kalman Filter

  • Jo, Sung-Woo (Materials & Components Basic Research Division, ETRI) ;
  • Jung, Sun-Kyu (Materials & Components Basic Research Division, ETRI) ;
  • Kim, Hyun-Tak (Materials & Components Basic Research Division, ETRI)
  • 투고 : 2020.03.24
  • 심사 : 2020.06.20
  • 발행 : 2020.06.28
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 확장 칼만 필터를 이용한 SOC 추정이 가능한 배터리 모니터링 시스템을 개발하였다. 배터리의 충·방전 상태를 정확하게 추정하기 위해 배터리 셀을 테브닌 모델로 모형화하고, 모델에 맞는 파라미터를 추출하였다. 이를 이용하여 확장 칼만 필터 알고리즘을 이용한 SOC 추정이 가능한 배터리 모니터링 디바이스를 제작하였으며, 다중 배터리 모니터링 디바이스 제어 및 배터리 상태 측정이 가능한 모니터링 서버를 제작하였다. 또한, 관리자가 실시간으로 상태를 확인하며 배터리 모니터링 디바이스 제어가 가능한 웹 서비스를 제작하였다. 특히, 배터리 SOC를 각각의 배터리 모니터링 디바이스에서 추정하고, 최종 결과만 모니터링 서버로 전달함으로써 서버의 계산량을 줄일 수 있다.

A Battery Monitoring System capable of State-of-Charge(SOC) estimation using the Extended Kalman Filter(EKF) is described in this paper. In order to accurately estimate the SOC of the battery, the battery cells were modeled as the Thevenin equivalent circuit model. The Thevenin model's parameters were measured in experiments. For the Battery Monitoring System, we designed a battery monitoring device that can calculate the SOC estimation using the EKF and a monitoring server that controls multiple battery monitoring devices. We also develop a web-based dashboard for controlling and monitoring batteries. Especially the computation of the monitoring server could be reduced by calculating the battery SOC estimation at each Battery Monitoring Device.

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참고문헌

  1. Y. H. You, C. S. Leem & D. S. Choi. (2018). Assessing the accuracy of electric energy monitoring system. Journal of the Korea Convergence Society, 9(9), 53-60. DOI : 10.15207/JKCS.2018.9.9.053
  2. H. K. Lee & G. T. Kim. (2019). Battery Monitoring System for High Capacity Uninterruptible Power Supply. Journal of IKEEE, 23(2), 580-585. DOI : 10.7471/ikeee.2019.23.2.580
  3. G. L. Plett. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1. Background. Journal of Power Sources, 134(2), 252-261. DOI : 10.1016/j.jpowsour.2004.02.031
  4. G. L. Plett. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation. Journal of Power Sources, 134(2), 277-292. DOI : 10.1016/j.jpowsour.2004.02.033
  5. J. Meng et al. (2018). An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery. IEEE Transactions on Industry Applications, 54(2), 1583-1591. DOI : 10.1109/TIA.2017.2775179
  6. R. Zhang et al. (2018). State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles. Energies, 11(7), 1820. DOI : 10.3390/en11071820
  7. Y. Zheng, M. Ouyang, X. Han, L. Lu & J. Li. (2018). Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles. Journal of Power Sources, 377, 161-188. DOI : 10.1016/j.jpowsour.2017.11.094
  8. J. H. Seung, J. K. Park & S. G. Yoo. (2018). Unknown-Parameter Identification for Accurate Control of 2-Link Manipulator using Dual Extended Kalman Filter. Journal of the Korea Convergence Society, 9(6), 53-60. DOI : 10.15207/JKCS.2018.9.6.053
  9. K. W. Jang & G. B. Chung. (2012). A SOC Estimation using Kalman Filter for Lithium-Polymer Battery. The Transactions of the Korean Institute of Power Electronics, 17(3), 222-229. DOI : 10.6113/TKPE.2012.17.3.222
  10. J. Meng, G. Luo, M. Ricco, M. Swierczynski, D. I. Stroe & R. Teodorescu. (2018). Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles. Applied Sciences, 8(5), 659. DOI : 10.3390/app8050659
  11. S. Jung. (2017). EKF Based SOH State Estimation Algorithm for UAV Li-Po Battery Pack. Journal of the Korea Convergence Society, 8(6), 237-243. DOI : 10.15207/JKCS.2017.8.6.237
  12. P. Shrivastava, T. K. Soon, M. Y. I. B. Idris & S. Mekhilef. (2019). Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries. Renewable and Sustainable Energy Reviews, 113, 109233. DOI : 10.1016/j.rser.2019.06.040
  13. L. Zhi, Z. Peng, W. Zhifu, S. Qiang & R. Yinan. (2017). State of Charge Estimation for Li-ion Battery Based on Extended Kalman Filter. Energy Procedia, 105, 3515-3520. DOI : 10.1016/j.egypro.2017.03.806
  14. P. Kim. (2011). Kalman Filter for Beginners: with MATLAB Examples. South Carolina : CreateSpace Independent Publishing Platform.
  15. J. H. Seo, K. R. Park & M. S. Yoon. (2017). Design and Implementation of web-based production monitoring system for small manufacturers. Journal of the Korea Convergence Society, 8(8), 89-94. DOI : 10.15207/JKCS.2017.8.8.089