Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

A Nonlinear Observer Design for Estimating State-of-Charge of Lithium Polymer Battery

리튬폴리머 배터리 잔존충전용량 추정을 위한 비선형 관측기 설계

  • 류석환 (대구대학교 전자전기공학부)
  • Received : 2012.03.06
  • Accepted : 2012.05.07
  • Published : 2012.06.25
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents a nonlinear observer design method for SOC(state-of-charge) estimation of Lithium polymer battery cell. The dynamic equation of the battery cell is modeled as a simple RC electrical circuit with a nonlinear voltage source and the parameters are obtained via nonlinear optimization. Using the sum of squares decomposition, the observer gain is designed such that the error dynamics is asymptotically stable and the decay rate is below the prescribed value. In order to illustrate the performance of the observer, a computer simulation is performed using the experimental data with the UDDS(urban dynamometer driving schedule) current profile.

본 논문은 리튬 폴리머 배터리 셀의 잔존충전용량을 추정하기 위한 비선형 관측기의 설계방법을 제시한다. 배터리 셀의 동적방정식은 비선형 전압원을 갖는 간단한 RC 전기회로로 모델하고 파라메터는 비선형 최적화기법을 이용하여 구한다. 관측기 이득은 제곱합 분해기법을 사용하여 오차의 동적방정식이 점근적으로 안정하고 추정오차 감소율이 설계자가 지정한 값 이하가 되도록 설계한다. 관측기의 성능을 입증하기 위하여 UDDS 전류 프로파일을 사용한 실험 데이터를 이용하여 모의실험을 수행하였다.

Keywords

References

  1. B. Pattipati, K. Pattipati, J. Christopherson, A. Namburu, D. Prokhorov and L. Qiao, "Automotive Battery Management Systems," IEEE Autotestcon, pp. 581-586, Salt Lake city, UT, Sep. 2008.
  2. G. Plett, "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 1. Background," Journal of power sources, pp. 252-261, Vol. 134, 2004. https://doi.org/10.1016/j.jpowsour.2004.02.031
  3. G. Plett, "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 2. Modeling and Identification," Journal of power sources, pp. 262-276, Vol. 134, 2004. https://doi.org/10.1016/j.jpowsour.2004.02.032
  4. G. Plett, "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 3. State and parameter estimation," Journal of power sources, pp. 277-292, Vol. 134, 2004. https://doi.org/10.1016/j.jpowsour.2004.02.033
  5. W. Yan, Y. Tian-ming and L. Bao-jie, "Lead-acid Power Battery Management System Basing on Kalman Filtering," IEEE Vehicle Power and Propulsion Conference, Harbin, China, Sep. 2008.
  6. B. Bhangu, P. Bentley, D. Stone and C. Bingham, "Nonlinear Observers for Predicting State-of-Charge and State-of-Health of Lead-Acid Batteries for Hybrid Electric Vehicles," IEEE Trans. on Vehicular Technology, Vol. 54, No. 3, pp.783-794, May 2005. https://doi.org/10.1109/TVT.2004.842461
  7. C. Gould, C. Bingham C. Stone and D. Bentley, "New Battery Model and State-of-Health Determination Through Subspace Parameter Estimation and State Observer Techniques," IEEE Trans. on Vehicular Technology, Vol. 58, No.8, pp.3905-3916, Oct. 2009. https://doi.org/10.1109/TVT.2009.2028348
  8. A. Papachristodoulou and S. Prajna, "Analysis of Non-polynomial Systems using the Sum of Squares Decomposition", pp. 1-20, Positive Polynomials in Control, Springer, 2005.
  9. K. Tanaka, K. Yamauchi, H. Ohtake and H. O. Wang, "Guaranteed Cost Control of Polynomial Fuzzy Systems via a Sum of Squares Approach", Proceedings of the 46th IEEE Conference on Decision and Control, pp.5954-5959, New Orleans, LA, Dec. 12-14, 2007.
  10. K. Tanaka, H. Ohtake, M. Wada, H. O. Wang and Y. Chen, "Polynomial Fuzzy Observer Design: A Sum of Squares Approach", Proceedings of the 48th IEEE Conference on Decision and Control, pp. 7771-7776, Shanghai, P.R.China, Dec. 16-18, 2009.

Cited by

  1. Robust High-Gain Observer Based SOC Estimator for Uncertain RC Model of Li-Ion Batteries vol.23, pp.3, 2013, https://doi.org/10.5391/JKIIS.2013.23.3.214
  2. Generation of Daily Load Curves for Performance Improvement of Power System Peak-Shaving vol.24, pp.2, 2014, https://doi.org/10.5391/JKIIS.2014.24.2.141
  3. Estimation of State-of-charge and Sensor Fault Detection of a Lithium-ion Battery in Electric Vehicles vol.63, pp.8, 2014, https://doi.org/10.5370/KIEE.2014.63.8.1085