Improved Method for State of Charge Estimation of Lithium Iron Phosphate Power Batteries

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Abstract:

To resolve the problems that the initial state of charge (SOC) and the available capacity of batteries are difficult to estimate when using the Ah counting method, in this paper An improved SOC estimation method was proposed that combined with the open circuit voltage (OCV) method and Ah counting method based on the analysis and consideration of the battery available capacity variation caused by charge and discharge current, environment temperature and battery state of health (SOH). The precision of the proposed method was validated by using Federal Urban Driving Schedule (FUDS) test of a Lithium iron phosphate (LiFePO4) power battery. The SOC estimate error using the proposed method relative to a discharge test was better than the Ah counting method.

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Periodical:

Advanced Materials Research (Volumes 712-715)

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1956-1959

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June 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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

DOI: 10.1109/pesw.2000.850009

Google Scholar

[2] MA Youliang, CHEN Quanshi, QI Zhanning. Research on the SOC definition and measurement method of batteries used in EVs [J] . J T sing hua Univ ( Sci & Tech) , 2001, 41( 11): 95-97 (In Chinese)

Google Scholar

[3] LIN Chengtao, WANG Junping, CHEN Quanshi. Methods for state of charge estimation of EV batteries and their application [J] . Battery Bimonthly, 2004, (5): 376-378 (In Chinese)

Google Scholar

[4] LI Guohong, WU Jingzhen, LIU Luyuan. SOC Estimation for Traction Battery Based on RC Circuit[J]. Journal of Tianjin University, 2007, 40(12):1453-1457 (In Chinese)

Google Scholar

[5] LEI Xiao, CHEN Qingquan, LIU Kaipei. Battery state of charge Estimation Based on Neural-Network for Electric Vehicles [J]. TRANSACTIONS OF CHINA ELECTROTECHNICAL SOCIETY, 2007,22(8): 155-160 (In Chinese)

Google Scholar

[6] 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)

Google Scholar

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

DOI: 10.2172/214312

Google Scholar