Publisher : The Korean Institute of Power Electronics
DOI : 10.6113/JPE.2016.16.1.27
Title & Authors
A Mixed SOC Estimation Algorithm with High Accuracy in Various Driving Patterns of EVs Lim, Dong-Jin; Ahn, Jung-Hoon; Kim, Dong-Hee; Lee, Byoung Kuk;
In this paper, a mixed algorithm is proposed to overcome the limitations of the conventional algorithms, which cannot be applied in various driving patterns of drivers. The proposed algorithm based on the coulomb counting method is mixed with reset algorithms that consist of the enhanced OCV reset method and the DCIR iterative calculation method. It has many advantages, such as a simple model structure, low computational overload in various profiles, and a low accumulated SOC error through the frequent SOC reset. In addition, the enhanced parameter based on a mathematical analysis of the second-order RC ladder model is calculated and is then applied to all of the methods. The proposed algorithm is verified by experimental results based on a 27-Ah LiPB. It is observed that the SOC RMSE of the proposed algorithm decreases by about 9.16% compared to the coulomb counting method.
K. W. E. Cheong, B. P. Divakar, H. J. Wu, K. D. K. Ding, and H. F. Ho, “Battery-management system (BMS) and SOC development for electrical vehicles,” IEEE Trans. Veh. Technol., Vol. 60, No. 1, pp. 76-88, Jan. 2011.
D. J. Lim, J. G. Kim, J. H. Ahn, D. H. Kim, and B. K. Lee, "A mixed SOC estimation algorithm using enhanced OCV reset and the DCIR iterative calculation reset," IEEE Conference on ECCE Asia (ICPE-ECCE Asia), pp. 1155-1160, 2015.
A. Manenti, A. Abba, A. Merati, S. M. Savaresi, and A. Geraci, “A new BMS architecture based on cell redundancy,” IEEE Trans. Ind. Electron., Vol. 58, No. 9, pp. 4314-4322, Sep. 2011.
J. Xu, C. C. Mi, B. Cao, J. Deng, Z. Chen, and S. Li, “The state of charge estimation of lithium-ion batteries based on a proportional-integral observer,” IEEE Trans. Veh. Technol., Vol. 63, No. 4, pp. 1614-1621, May 2014.
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, Jan. 2001.
K. S. Ng, C. S. Moo, Y. P. Chen, and Y. C. Hsieh, “Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries,” Journal of Applied Energy, Vol. 86, No. 9, pp. 1506-1511, Sep. 2009.
G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background,” Journal of Power Sources, Vol. 134, No. 2, pp. 252-261, Aug. 2004.
D. Xu, X. Huo, X. Bao, C. Yang, H. Chen, and B, Cao, "Improved EKF for SOC of the storage battery," IEEE International Conference on Mechatronics and Automation, pp. 1497-1501, 2013.
S. Sepasi, R. Ghorbani, and B. Y. Liaw, “A novel on-board state-of-charge estimation method for aged Li-ion batteries based on model adaptive extended Kalman filter,” Journal of Power Sources, Vol. 245, pp. 337-344, Jan. 2014.
R. Xiong, X. Gong, C. C. Mi, and F. Sum, “A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter,” Journal of Power Sources, Vol. 243, pp. 805-816, Dec. 2013.
C. Mohammad and F. Mohammad, “State-of-charge estimation for lithium-ion batteries using neural networks and EKF,” IEEE Transaction on Industrial Electronics, Vol. 57, No. 12, pp. 4178-4187, Dec. 2010.
Z. Chen and Y. Fu, “State of charge estimation of lithium-ion batteries in electric drive vehicles using extended Kalman filtering,” IEEE Trans. Veh. Technol., Vol. 62, No. 3, pp. 1020-1030, Mar. 2013.
X. Lu, K. L. V. Iyer, K. Mukherjee, and N. C. Kar, “A dual purpose triangular neural network based module for monitoring and protection in bi-directional off-board level-3 charging of EV/PHEV,” IEEE Trans. Smart Grid, Vol. 3, pp. 1670-1678, Aug. 2012.
I. H. Li, W. Y. Wang, S. F. Su, and Y. S. Lee, “A merged fuzzy neural network and its applications in battery state-of-charge estimation,” IEEE Trans. Energy Convers., Vol. 22, No. 3, pp. 697-708, Sep. 2007.
T. Weigert, Q. Tian, and K. Lian, “State-of-charge prediction of batteries and battery–supercapacitor hybrids using artificial neural networks,” Journal of Power Sources, Vol. 196, pp. 4061-4066, Apr. 2011.
Y. M. Jeong, Y. K. Cho, J. H. Ahn, S. H. Ryu, and B. K. Lee, “Enhanced coulomb counting method with adaptive SOC reset time for estimating OCV,” Energy Conversion Congress and Exposition, pp. 4313-4318, 2014.
Y. K. Cho, Y. M. Jeong, J. H. Ahn, S. H. Ryu, and B. K. Lee, “A new SOC estimation algorithm without integrated error using DCIR repetitive calculation,” International Conference on Electrical Machines and Systems, pp. 865-870, 2014.
S. R. Lee, B. O. Lim, J. R. Ha, and W. B. Kim, Automotive Engineering, Bosungkak, Chap. 4, 1996.
H. Rahimi-Eichi, F. Baronti, and M. Y. Chow, “Online adaptive parameter identification and state-of-charge coestimation for lithium-polymer battery cells,” IEEE Trans. Ind. Electron., Vol. 61, No. 4, pp. 2053-2061, Apr. 2014.
Z. Miao, L. Xu, V. R. Disfani, and L. Fan, “An SOC-based battery management system for microgrids,” IEEE Trans. Smart Grid, Vol. 5, No. 2, pp. 966-973, Mar. 2014.
W. Wang, H. S.-H. Chung, and J. Zhang, “Near-real-time parameter estimation of an electrical battery model with multiple time constants and SOC-dependent capacitance,” IEEE Trans. Power Electron., Vol. 29, No. 11, pp. 5905-5920, Nov. 2014.