Publisher : The Korean Institute of Power Electronics
DOI : 10.6113/JPE.2016.16.1.217
Title & Authors
Analysis of Real-Time Estimation Method Based on Hidden Markov Models for Battery System States of Health Piao, Changhao; Li, Zuncheng; Lu, Sheng; Jin, Zhekui; Cho, Chongdu;
A new method is proposed based on a hidden Markov model (HMM) to estimate and analyze battery states of health. Battery system health states are defined according to the relationship between internal resistance and lifetime of cells. The source data (terminal voltages and currents) can be obtained from vehicular battery models. A characteristic value extraction method is proposed for HMM. A recognition framework and testing datasets are built to test the estimation rates of different states. Test results show that the estimation rates achieved based on this method are above 90% under single conditions. The method achieves the same results under hybrid conditions. We can also use the HMMs that correspond to hybrid conditions to estimate the states under a single condition. Therefore, this method can achieve the purpose of the study in estimating battery life states. Only voltage and current are used in this method, thereby establishing its simplicity compared with other methods. The batteries can also be tested online, and the method can be used for online prediction.
C. C. Chan and Y. S. Wong, “Electric vehicles charge forward,” Power and Energy Magazine , Vol. 2, No. 6, pp. 24-33, Dec. 2004.
T. Horiba, "Lithium-ion battery system," in Proc. the IEEE, Vol. 102, No. 6, pp. 936-950, 2014.
P. Bubna, D. Brunner, S. G. Advani, and A. K. Prasad, “Prediction-based optimal power management in a fuel cell/battery plug-in hybrid vehicle,” J. Power Sources, Vol. 195, No. 19, pp. 6699-6708, Oct. 2010.
E. Meissner, and G. Richter, “The challenge to the automotive battery industry: the battery has to become an increasingly integrated component within the vehicle electric power system,” J. Power Sources, Vol. 144, No. 2, pp. 438-460, Jun. 2005.
J. A. M. Penna and C. L. N. Júnior, "Health monitoring and remaining useful life estimation of lithium-ion aeronautical batteries," in Aerospace Conference. IEEE, 2012, pp. 1-12, Mar. 2012.
Q. Zhang and R. E.White. “Capacity fade analysis of lithium ion cell,” J. Power Sources, Vol. 179, No. 2, pp. 793-798, May 2008.
D. Rakhmatov, S. Vrudhula, and D. A. Wallach, “A model for battery lifetime analysis for organizing applications on a pocket computer,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., Vol. 11, No. 6, pp. 1019-1030, Dec. 2003.
C. H. Piao, Z. Huang, L. Su, and S. Lu, “Research on outlier detection algorithm for evaluation of battery system safety,” Adv. Mech Eng., Vol. 6, pp. 1-8, Dec. 2014.
Y. Xie, Z. G. Wang, and Y. S. Zhang, “Development of battery system testing machine,” International Journal of Future Engineering, Vol. 2015, pp. 1-7, 2015.
C. H. Piao, Z. G. Wang, J. Cao, W. Zhang, and S. Lu, “Lithium-ion battery cell-balancing algorithm for battery management system based on real-time outlier detection,” Math Probl Eng, Vol. 4, pp. 1-13, Apr. 2015.
C. H. Piao, Q. F. Yu, C. X. Duan, L. Su, and Y. Zhang, “Virtual environment modeling for battery management system,” J. Electr. Eng. Technol., Vol. 9, No. 8, pp. 1729-1738, Sep. 2014.
T. Parthiban, R. Ravi, and N. Kalaiselvi, “Exploration of artificial neural network [ANN] to predict the electrochemical characteristics of lithium-ion cells,” Electrochim. Acta, Vol. 53, No. 4, pp. 1877-1882, Dec. 2007.
B. Y. Liaw, R. G. Jungst, and G. Nagasubramanian, “Modeling capacity fade in lithium-ion cells,” J. Power Sources, Vol.140, No.1, pp. 157-161, Jan. 2005.
X. S. Hu, F. C. Sun, Y. Zou, and H. Peng, "Online estimation of an electric vehicle lithium-ion battery using recursive least squares with forgetting," in American Control Conference (ACC), pp. 935-940, Jul.2011.
W. Zhang, C. Cho, J. Liu, and X. Han, “A hybrid parameter identification method based on probability and interval for uncertainty structures”. Mechanical Systems and Signal Processing, Vol. 60, pp. 853-865, 2015.
W. Zhang, C. Cho, J. Liu, and X. Han, “A fast bayesian approach for parameter identifications using adaptive densifying approximation technique,” Inverse Probl. Sci. Eng, Mar. 2015. (online)
Z. J. Liu, Q. Li, X. H. Liu, and C. D. Mu, “A hybrid LSSVR/HMM-based prognostic approach,” Sensors, Vol.13, No.5, pp. 5542-5560, Apr. 2013.
L. R. Rabiner, "A tutorial on hidden Makrov models and selected applications in speech recognition," in Proc. the IEEE, Vol.77, No. 1, pp. 257-286, Feb. 1989.
L. R. Rabiner. “An introduction to hidden Markov models,” IEEE ASSP Magazine, Vol. 3, No. 1, pp. 4-16, 1986.
C. H. Piao, W. L. Fu, G. H. Lei, and C. D. Cho, “Online parameter estimation of the Ni-MH batteries based on statistical methods,” Energies, Vol. 3, No. 2, pp. 206-215, Feb. 2010.
J. Kim, S. Lee, and B. Cho, "Discrimination of battery characteristics using discharging/charging voltage pattern recognition," Energy Conversion Congress and Exposition, IEEE. pp. 20-24, Sep. 2009.