Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
권호 표지
DOI QR코드

DOI QR Code

A State-of-Charge estimation using extended Kalman filter for battery of electric vehicle

확장칼만필터를 이용한 전기자동차용 배터리 SOC 추정

  • Ryu, Kyung-Sang (System Convergence Laboratory, Korea Institute of Energy Research) ;
  • Kim, Byungki (System Convergence Laboratory, Korea Institute of Energy Research) ;
  • Kim, Dae-Jin (System Convergence Laboratory, Korea Institute of Energy Research) ;
  • Jang, Moon-seok (System Convergence Laboratory, Korea Institute of Energy Research) ;
  • Ko, Hee-sang (System Convergence Laboratory, Korea Institute of Energy Research) ;
  • Kim, Ho-Chan (Dpartment of Electrical Engineering, Jeju National University)
  • 유경상 (한국에너지기술연구원 시스템융복합연구실) ;
  • 김병기 (한국에너지기술연구원 시스템융복합연구실) ;
  • 김대진 (한국에너지기술연구원 시스템융복합연구실) ;
  • 장문석 (한국에너지기술연구원 시스템융복합연구실) ;
  • 고희상 (한국에너지기술연구원 시스템융복합연구실) ;
  • 김호찬 (제주대학교 전기전자통신공학부)
  • Received : 2017.09.21
  • Accepted : 2017.10.13
  • Published : 2017.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper reports a SOC(State-of-Charge) estimation method using the extended Kalman filter(EKF) algorithm, which can allow real-time implementation and reduce the error of the model and be robust against noise, to accurately estimate and evaluate the charging/discharging state of the EV(Electric Vehicle) battery. The battery was modeled as the first order Thevenin model for the EKF algorithm and the parameters were derived through experiments. This paper proposes the changed method, which can have the SOC to 0% ~ 100% regardless of the aging of the battery by replacing the rated capacity specified in the battery with the maximum chargeable capacity. In addition, This paper proposes the EKF algorithm to estimate the non-linearity interval of the battery and simulation result based on Ah-counting shows that the proposed algorithm reduces the estimation error to less than 5% in all intervals of the SOC.

본 논문에서는 전기자동차용 배터리의 충방전 상태를 정확하게 추정하고 안정적으로 평가하기 위하여, 비선형성을 가지는 배터리의 출력특성을 단계마다 선형화시켜 상태를 평가하고, 실시간 구현 및 모델의 오차보정과 노이즈에 강인한 특성을 가지고 있는 확장칼만필터 알고리즘을 이용한 SOC 추정 방법을 제안한다. 확장칼만필터를 적용하기 위해 배터리를 1차 Thevenin 모델로 나타내고, SOC 추정을 위한 배터리 성능평가 시뮬레이터를 구현하여, 실험을 통해 확장칼만필터에 적용될 파라미터를 도출한다. 본 논문에 적용된 SOC 상태추정 전략에서는 기존 선행 연구들과 다르게 배터리에 명시되어 있는 정격용량을 최대 충전가능용량으로 대체함으로써, 배터리의 노화에 상관없이 언제나 0%~100%의 SOC를 가질 수 있도록 변경된 수법을 제안한다. 이를 통해, 고정밀 CT를 사용한 Ah counting에 의한 SOC 추정을 기준으로 하여 본 논문에서는 배터리의 비선형 구간에서도 오차를 줄일 수 있는 확장칼만필터 방법을 제안하고 시뮬레이션을 통해 배터리 전 SOC 영역에서 추정오차를 5% 미만으로 줄일 수 있음을 확인한다.

Keywords

References

  1. L. Lu and X. Han, "A review on the key issues for lithium-ion battery management in electric vehicles", Journal of power sources, vol. 226, pp. 272-288, 2013. DOI: https://doi.org/10.1016/j.jpowsour.2012.10.060
  2. B. Xia and C. Chen, "A novel method for state of charge estimation of lithium-ion batteries using a nonlinear observer", Journal of power sources, vol. 270, pp. 359-366, 2014. DOI: https://doi.org/10.1016/j.jpowsour.2014.07.103
  3. S. Piller, M. Perrin, and A. Jossen, "Methods for state-of-charge determination and their applications", Journal of power sources, vol. 96, pp. 113-120, 2001. DOI: https://doi.org/10.1016/S0378-7753(01)00560-2
  4. R. Klein, N. A. Chaturvedi, and J. Christensen, "Electrochemical model based observer design for a lithium-ion battery", IEEE Transactions on Control Systems Technology, vol. 21, pp. 289-301, 2012. DOI: https://doi.org/10.1109/TCST.2011.2178604
  5. B. Y. Liaw and G. Nagasubramanian, "Modeling of lithium ion cells-A simple equivalent-circuit model approach", Solid State Ionics, vol. 175, pp. 835-839, 2004. DOI: https://doi.org/10.1016/j.ssi.2004.09.049
  6. G. Welch and G. Bishop, "The Introduction to the Kalman Filter", Couse Note of University of North Carolina at Chapel Hill, 2001.
  7. F. L. Lewis, L. Xie, and D. Popa, "Optimal and Robust Estimation with an Intruduction to Stochastic Control Theory" CRC Press, 2003.
  8. R. E. Kalman and R. S. Busy, "New results in linear filtering and prediction theory," ASME Journal of Basic Engineering, Series D, vol. 83, pp. 95-108, 1961. DOI: https://doi.org/10.1115/1.3658902
  9. R. G. Brown and P. Y. C. Hwang, "Introduction to Random Signals and Applied Kalman Filtering", John Wiley and Sons, 1985.
  10. J. B. Burl, Linear Optimal Control, Addison Wesley, Menlo Park, California, 1999.
  11. F. L. Lewis and V. L. Syrmos. "Optimal Control, Second Edition", John Wiley and Sons, New York, 1995.
  12. G. Plett, "Extended Kalman filtering for battery management systems of LiPBbased HEV battery packs: Part 3. State and parameter estimation", Journal of Power Sources, vol. 134, pp. 277-292, 2004. DOI: https://doi.org/10.1016/j.jpowsour.2004.02.031
  13. D. Jwo and F. Chuang, "Adaptive Kalman Filter for Navigation Sensor Fusion", National Taiwan Ocean University, Sensor Fusion and its Applications, 2010.
  14. Hongwen He, Rui Xiong, and Jinxin Fan, "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," J. Energies, vol. 4, pp 582-598, ISSN 1996-1073, March 2011. DOI: https://doi.org/10.3390/en4040582