Browse > Article
http://dx.doi.org/10.6113/TKPE.2014.19.2.139

Novel Estimation Technique for the State-of-Charge of the Lead-Acid Battery by using EKF Considering Diffusion and Hysteresis Phenomenon  

Duong, Van-Huan (Dept. of Electrical Eng., Soongsil University)
Tran, Ngoc-Tham (Dept. of Electrical Eng., Soongsil University)
Park, Yong-Jin (Dept. of Electrical Eng., Soongsil University)
Choi, Woojin (Dept. of Electrical Eng., Soongsil University)
Publication Information
The Transactions of the Korean Institute of Power Electronics / v.19, no.2, 2014 , pp. 139-148 More about this Journal
Abstract
State-of-charge (SOC) is one of the significant indicators to estimate the driving range of the electric vehicle and to control the alternator of the conventional engine vehicles as well. Therefore its precise estimation is crucial not only for utilizing the energy effectively but also preventing critical situations happening to the power train and lengthening the lifetime of the battery. However, lead-acid battery is time-variant, highly nonlinear, and the hysteresis phenomenon causes large errors in estimation SOC of the battery especially under the frequent discharge/charge. This paper proposes a novel estimation technique for the SOC of the Lead-Acid battery by using a well-known Extended Kalman Filter (EKF) and an electrical equivalent circuit model of the Lead-Acid battery considering diffusion and hysteresis characteristics. The diffusion is considered by the reconstruction of the open circuit voltage decay depending on the rest time and the hysteresis effect is modeled by calculating the normalized integration of the charge throughput during the partial cycle. The validity of the proposed algorithm is verified through the experiments.
Keywords
extended Kalman filter; state-of charge estimation; hysteresis effect; diffusion effect; idle start-stop;
Citations & Related Records
연도 인용수 순위
  • Reference
1 D. Schrank, B. Eisele, and T. Lomax, "TTI's 2012 Urban Mobility Report," Texas A&M Transportation Institute, Annual Report, 2012.
2 G. L. Plett, "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification," Journal of Power Sources, Vol. 134, pp. 262-276, 2004.   DOI   ScienceOn
3 V. Pop, H. J. Bergveld, P. H. L. Notten, and P. P. L. Regtien, "State-of-the-art of battery state-of-charge determination," Measurement Science and Technology, Vol. 16, pp. R93, 2005.   DOI
4 H. Yiran and S. Yurkovich, "Battery state of charge estimation in automotive applications using LPV techniques," American Control Conference (ACC) , pp. 5043-5049, 2010.
5 M. Thele, J. Schiffer, E. Karden, E. Surewaard, and D. U. Sauer, "Modeling of the charge acceptance of lead- acid batteries," Journal of Power Sources, Vol. 168, pp. 31-39, 2007.   DOI   ScienceOn
6 M. A. Roscher and D. U. Sauer, "Dynamic electric behavior and open-circuit-voltage modeling of LiFePO4-based lithium ion secondary batteries," Journal of Power Sources, Vol. 196, pp. 331-336, 2011.   DOI
7 C. Min and G. A. Rincon-Mora, "Accurate electrical battery model capable of predicting runtime and I-V performance," Energy Conversion, IEEE Transactions on, Vol. 21, pp. 504-511, 2006.   DOI   ScienceOn
8 K. Brik and F. ben Ammar, "Causal tree analysis of depth degradation of the lead acid battery," Journal of Power Sources, Vol. 228, pp. 39-46, 2013.   DOI
9 S. Buller, M. Thele, E. Karden, and R. W. De Doncker, "Impedance-based non-linear dynamic battery modeling for automotive applications," Journal of Power Sources, Vol. 113, pp. 422-430, 2003.   DOI   ScienceOn
10 T. Xidong, Z. Xiaodong, B. Koch, and D. Frisch, "Modeling and estimation of Nickel Metal Hydride battery hysteresis for SOC estimation," Prognostics and Health Management, 2008. PHM 2008. International Conference on, 1-12, 2008.
11 M. Thele, S. Buller, D. U. Sauer, R. W. De Doncker, and E. Karden, "Hybrid modeling of lead-acid batteries in frequency and time domain," Journal of Power Sources, Vol. 144, pp. 461-466, 2005.   DOI
12 W. Guoliang, L. Rengui, Z. Chunbo, C.C.Chan, "State of Charge Estimation for NiMH Battery Based on Electromotive Force Method," Electric Information and Control Engineering (ICEICE), 2011 International Conference.
13 H. Dai, X. Wei, Z. Sun, J. Wang, and W. Gu, "Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications," Applied Energy, Vol. 95, pp. 227-237, 2012.   DOI
14 P. Mauracher and E. Karden, "Dynamic modelling of lead/acid batteries using impedance spectroscopy for parameter identification," Journal of Power Sources, Vol. 67, pp. 69-84, 1997.   DOI
15 H. Hongwen, X. Rui, Z. Xiaowei, S. Fengchun, and F. JinXin, "State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model," Vehicular Technology, IEEE Transactions on, Vol. 60, pp. 1461-1469, 2011.   DOI
16 S. Lee, J. Kim, J. Lee, and B. H. Cho, "State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge," Journal of Power Sources, Vol. 185, pp. 1367-1373, 2008.   DOI   ScienceOn
17 K. W. E. Cheng, B. P. Divakar, W. Hongjie, D. Kai, and H. Ho Fai, "Battery-Management System (BMS) and SOC Development for Electrical Vehicles," Vehicular Technology, IEEE Transactions on, Vol. 60, pp. 76-88, 2011.   DOI
18 N. A. Windarko and J. Choi, "Hysteresis modeling for estimation of State-of-Charge in NiMH battery based on improved Takacs model," Telecommunications Energy Conference, 2009. INTELEC 2009. 31st International, 1-6, 2009.
19 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, pp. 252-261, 2004.   DOI   ScienceOn
20 J. Lee, O. Nam and B.H. Cho, "Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering," Journal of Power Sources, Vol. 174, pp. 9-15, 2007.   DOI   ScienceOn
21 K. Taesic and Q. Wei, "A Hybrid Battery Model Capable of Capturing Dynamic Circuit Characteristics and Nonlinear Capacity Effects," Energy Conversion, IEEE Transactions on, Vol. 26, pp. 1172-1180, 2011.   DOI