A battery model including hysteresis for State-of-Charge estimation ...

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IEEE Vehicle Power and Propulsion Conference (VPPC), September 3-5, 2008, Harbin, ChinadUOCVvoltage test, andcould be calculateddSOCfrom the relationship curve of the OCV-SOC.V. EVALUATION OF THE MODEL ANDMETHOD OF SOC ESTIMATIONFirst, the presented Ni-MH battery model is evaluatedby comparing the measured and the simulated batteryvoltage corresponding to several current profiles. As thefigure 6, the battery was charged and discharged underpulse current (1C, about 60S). We used two differentmodels that one includes the hysteresis model and theother without. The comparison result indicated that thehysteresis model plays one great role in the Ni-MHbattery model.Voltage(V)1.501.481.461.441.421.401.381.361.341.321.301.28Measured VoltageSimulated Voltage(no Hysteresis Model)Simulated Voltage1.260 200 400 600 800 1000 1200 1400Time(s)Figure6. Comparison of the measured voltage with the model simulatedvoltage, one simulation model including the hysteresis model and theother one without the hysteresis modelIn the figure 7, the measured battery voltage wascompared with the model simulation voltage under onerandom current load.Vol t age( V)46444240383634Mode l Si mul at i on Vol t ageMeasur ed Vol t age0 500 1000 1500 2000 2500 3000 3500 4000Ti me( S)1.3701.3651.3601.355600 640 680Figure7. Comparison of measured and simulated voltage correspondingto one random current profileSecond, the behavior of the battery SOC algorithmwhich is based on the presented Ni-MH battery model isalso evaluated under one random discharge/chargecurrent test. The Figure 8 is the current profile of the testand figure9 is the SOC estimation result of using themethod EKF and Ah-integration respectively.Cur r ent ( A)40200-20-40-60-801000 2000 3000 4000 5000 6000 7000 8000 9000Ti me( S)Figure8. Input random current profiles to evaluate the SOC estimationalgorithmSOC( %)6050403020100-10-20-301000 2000 3000 4000 5000 6000 7000 8000 9000Ti me( S)EKFAh- i nt egr at i onFigure9. Comparison of the SOC estimation using EKF andAh-integration respectively under the current loads as the figure 8In the Figure 9, the SOC Estimation results werecompared with the Ah-integration method which canreach one certain accuracy in one short time. We couldrecognize that there is the obvious deviation between thetwo estimation results. We could see that the estimationresult of the Ah-integration method reach to one negativevalue when the battery was nearly to be fully discharged.Apparently, the EKF estimation method avoided thedeficit that the estimation errors have been integratedgradually during the test in the Ah-integration method,and one more accurate estimation result could be gained.VI. CONCLUTIONIn this paper one equivalent circuit model includingthe hysteresis voltage model for Ni-MH battery has beenpresented. We used the method of EIS to identify theNi-MH battery model structure. Then combined withsome current-voltage response tests, the basic Ni-MHCDABAuthorized licensed use limited to: GOVERNMENT COLLEGE OF TECHNOLOGY. Downloaded on December 31, 2009 at 04:49 from IEEE Xplore. Restrictions apply.
IEEE Vehicle Power and Propulsion Conference (VPPC), September 3-5, 2008, Harbin, Chinabattery model including the hysteresis was established.And then an EKF based algorithm has been developed toestimate SOC. The proposed method can accuratelymodel the dynamic behavior of the battery which makesit appropriate for HEV application. Finally, the capabilityand accuracy of the proposed method in estimation SOCof the Ni-MH batteries is verified by the evaluationworks.VII. ACKNOWLEDGMENTThis work was greatly supported by the ShanghaiScience and Technology Commission of Shanghai andShanghai Fuel Cell Vehicle Powertrain Co., Ltd. Thebattery test laboratory in Automobile College, TongjiUniversity, provided the test facilities used in the wholeperiod of investigation. Many enlightening discussionswith Pro. Sun Zechang, Associate Pro. Wei Xuezhe andPh.D Dai Haifeng are gratefully acknowledged. Besidesmany colleagues such as Yuan Yongjun, Wang Jiayuanare also acknowledged.REFERENCES[1] Chen Quanshi, Lin Chengtao. “Summarization of studies onperformance models of batteries for electric vehicle”. AutomobileTechnology, 2005, 37(3):1-5.[2] S.Piller, M.Perrin, A.Jossen. “Methods for state-of-chargedetermination and their application”. Journal of Power Sources,96: (2001)113-120.[3] Marc Thele, Oliver Bohlen, Dirk Uwe Sauer. “Development of avoltage-behavior model for NiMH batteries using animpedance-based modeling concept”. J. Power Sources, 2008,175 (2008) 635-643.[4] Venkat Srinivasan, John W.Weidner, and John Newman.“Hysteresis during Cycling of Nickel Hydride Active Material”. J.Electrochemical Society, 2001, 148(9):696-980.[5] M.W. Verbrugge, Edwad Tate. “Adaptive state of chargealgorithm for nickel metal hydride battery including hysteresisphenomena”. J of Power Sources,2004,126: 236-249.[6] Feng Xuyun, Wei Xuezhe. “Study on the Ni-MH BatteryEquivalent Circuit model. Battery”, 2007, Vol.37(4):286-288.[7] S.Buller. “Impedence-based simulation models for energy storagedevices in advanced automotive power system”. PhD Thesis,RWTH Aachen Univercity, 2002, ISBN: 3-8322-1225-6.[8] Wu Hongjie, Qi Bojin, Zheng Minxin, Liu Yongzhe. “Ni-MHbattery state-of-charge estimation based on Kalman filter”. J.Beijing University of Aeronautics and Astronautics, 2007,33(8):945-948.[9] Deng zili. Kalman filter and Vina filter [M]. Harbin: HarbinIndustry University Publisher, 2001.Authorized licensed use limited to: GOVERNMENT COLLEGE OF TECHNOLOGY. Downloaded on December 31, 2009 at 04:49 from IEEE Xplore. Restrictions apply.
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