Lithium Iron Phosphate Power Battery State of Charge Estimation Based on the Extended Kalman Filter

Abstract:

In order to estimate the state of charge (SOC) of lithium iron phosphate (LiFePO4) power battery, the state space model that fit for kalman filter to estimate was established on the basis of PNGV equivalent circuit model. In the case that considering the influence factors such as power battery charge and discharge current, environmental temperature and battery state of health, an improved composite SOC estimation algorithm based on extended kalman filter (EKF) algorithm was proposed, this proposed algorithm integrated using EKF algorithm, improved Ah counting method and open circuit voltage method to estimate SOC. The simulation results show that the proposed algorithm can track the change of the power battery SOC effectively, verify the validity of the proposed algorithm.

• Periodical: Applied Mechanics and Materials (Volumes 336-338)
• Pages: 784-788
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.336-338.784
• Online since: July 2013
• Authors: Ming Li, Yang Jiang, Jian Zhong Zheng, Xiao Xiao Peng
• Keywords: Battery Model, Extended Kalman Flter (EKF), Lithium Iron Phosphate, Power Battery, State of Charge (SOC)

References

[1] Chen Quanshi. Advanced Electric vehicles Technology[M]. Chemical Industry Press, Beijing, 2009 (In Chinese)

[2] MA Youliang, CHEN Quanshi, QI Zhanning. Research on the SOC definition and measurement method of batteries used in EVs [J] . Journal of Tsinghua University (Science and Technology), 2001, 41(11): 95-97 (In Chinese)

[3] ROBERT F. Power requirements for batteries in hybrid electric vehicles[J]. Journal of Power Sources, 2000, 91: 2-6

[4] H.L. Chan, D.Sutanto. A New Battery Model for use with Battery Energy Storage Systems and Electric Vehieles Power Systems. IEEE Power Engineering Society Winter Meeting, Singapore, 2000, 470-475

DOI: 10.1109/pesw.2000.850009

[5] O'GORMAN C C, INGERSOLL D, JUNGST R G, et al. Artificial neural network simulation of battery performance[C]. Proc 31st Annual Hawaii International Conference on System Sciences, Hawaii: 1998, 461-472

DOI: 10.1109/hicss.1998.648303

[6] G. L. Plett. Extended Kalman filtering for battery management systems of LiPB -based HEV battery packs. Part 3: State and parameter estimation. Journal of Power Sources, 2004, 134 (2): 277-292

DOI: 10.1016/j.jpowsour.2004.02.033

[7] SUN Jingxia, TAN Derong. Estimate State of Charge of Power Lithium-ion Batteries Based on Kalman Filtering for Electric Vechicle [J], Agricultural equipment & Vehicle engineering, 2010, 9: 20-23 (In Chinese)

[8] FreedomCAR Battery Test Manual for Power-Assist Hybrid Electrical Vehicle. October, (2003)

[9] USABC Electric Vehicle Battery Test Procedure Manual [R]. Revision 2, DOE/ ID-10479, January (1996)

DOI: 10.2172/214312