A Novel Hybrid Technique to Predict the Lithium-Ion Battery’s Behavior and Estimate the Intern Impedance

References

[1] Sidhu A, Izadian A, Anwar S. Adaptive nonlinear model-based fault diagnosis of li-ion batteries. IEEE Trans. Ind Electron. 2015;62(2):1002–1011.10.1109/TIE.2014.2336599Suche in Google Scholar

[2] Dees DW, Battaglia VS, B´elanger A. Electrochemical modeling of lithium polymer batteries. J. Power Sources. 2002;110(2):310–320.10.1016/S0378-7753(02)00193-3Suche in Google Scholar

[3] Kennedy B, Patterson D, Camilleri S. Use of lithium-ion batteries in electric vehicles. J Power Sources. 2000;90(2):156–162.10.1016/S0378-7753(00)00402-XSuche in Google Scholar

[4] Xu J, Mi CC, Cao B, Deng J, Chen Z, Li S. The state of charge estimation of lithium-ion batteries based on a proportional-integral observer. IEEE Trans Veh Technol. 2014;63(4):1614–1621.10.1109/TVT.2013.2287375Suche in Google Scholar

[5] Erdinc O, Vural B, Uzunoglu M. A dynamic lithium-ion battery model considering the effects of temperature and capacity fading. In: Clean Electrical Power, 2009 International Conference on, IEEE 2009:383–386.10.1109/ICCEP.2009.5212025Suche in Google Scholar

[6] Shafiei A, Momeni A, Williamson SS. Battery modeling approaches and management techniques for plug-in hybrid electric vehicles. In: Vehicle Power and Propulsion Conference (VPPC), 2011 IEEE, IEEE 2011:1–5.10.1109/VPPC.2011.6043191Suche in Google Scholar

[7] Liu G, Lu L, Li J, Ouyang M. Thermal modeling of a lifepo 4/graphite battery and research on the influence of battery temperature rise on ev driving range estimation. In: Vehicle Power and Propulsion Conference (VPPC), 2013 IEEE, IEEE 2013:1–5.Suche in Google Scholar

[8] Lu L, Han X, Li J, Hua J, Ouyang M. A review on the key issues for lithiumion battery management in electric vehicles. Journal of power sources. 2013;226:272–288.10.1016/j.jpowsour.2012.10.060Suche in Google Scholar

[9] Rong P, Pedram M. An analytical model for predicting the remaining battery capacity of lithium-ion batteries. IEEE Trans Very Large Scale Integr VLSI Syst. 2006;14(5):441–445.10.1109/TVLSI.2006.876094Suche in Google Scholar

[10] Chen M, Rincon-Mora GA. Accurate electrical battery model capable of predicting runtime and iv performance. IEEE Trans. Energy Convers. 2006;21(2):504–511.10.1109/TEC.2006.874229Suche in Google Scholar

[11] Thanagasundram S, Arunachala R, Makinejad K, Teutsch T, Jossen A. A cell level model for battery simulation. In: European Electric Vehicle Congress (EEVC) 2012.Suche in Google Scholar

[12] Hajizadeh A, Golkar MA. Intelligent power management strategy of hybrid distributed generation system. Int J Electr Power Energy Syst. 2007;29(10):783–795.10.1016/j.ijepes.2007.06.025Suche in Google Scholar

[13] Buller S, Thele M, De Doncker R, Karden E. Impedance-based simulation models of supercapacitors and li-ion batteries for power electronic applications.0020IEEE. Trans Ind Appl. 2005;41(3):742–747.10.1109/TIA.2005.847280Suche in Google Scholar

[14] Hammad NM. Automotive battery modelling and management. J Eng Sci Technol Rev. 2011;4(2):140–145.10.25103/jestr.042.05Suche in Google Scholar

[15] Liaw BY, Nagasubramanian G, Jungst RG, Doughty DH. Modeling of lithium ion cells–a simple equivalent-circuit model approach. Solid State Ionics. 2004;175(1):835–839.10.1016/j.ssi.2004.09.049Suche in Google Scholar

[16] Newman J, Thomas-Alyea KE. Electrochemical systems. John Wiley & Sons, 2012.Suche in Google Scholar

[17] Newman J, Thomas KE, Hafezi H, Wheeler DR. Modeling of lithium-ion batteries. J Power Sources. 2003;119:838–843.10.1016/S0378-7753(03)00282-9Suche in Google Scholar

[18] Parthiban T, Ravi R, Kalaiselvi N. Exploration of artificial neural network [ann] to predict the electrochemical characteristics of lithium-ion cells. Electrochim. Acta. 2007;53(4):1877–1882.10.1016/j.electacta.2007.08.049Suche in Google Scholar

[19] Hussein AA. Capacity fade estimation in electric vehicle li-ion batteries using artificial neural networks. IEEE Trans. Ind. Appl. 2015;51(3):2321–2330.10.1109/TIA.2014.2365152Suche in Google Scholar

[20] Naidu D. Neuro-fuzzy and soft computing–a computational ap-proach to learning and machine intelligence–jsr jang 1997.Suche in Google Scholar

[21] Mesbahi T, Khenfri F, Rizoug N, Chaaban K, Bartholome¨us P, Le Moigne P. Dynamical modeling of li-ion batteries for electric vehicle applications based on hybrid particle swarm–nelder–mead (pso–nm) optimization algorithm. Electric Power Syst Res. 2016;131:195–204.10.1016/j.epsr.2015.10.018Suche in Google Scholar

[22] Kroeze RC, Krein PT. Electrical battery model for use in dynamic electric vehicle simulations. In: Power Electronics Specialists Conference, 2008. PESC 2008. IEEE, IEEE. 2008;1336–1342.10.1109/PESC.2008.4592119Suche in Google Scholar

[23] Einhorn M, Conte FV, Kral C, Fleig J. Comparison, selection, and parameterization of electrical battery models for automotive applications. IEEE Trans Power Electron. 2013;28(3):1429–1437.10.1109/TPEL.2012.2210564Suche in Google Scholar

[24] Brand J, Zhang Z, Agarwal RK. Extraction of battery parameters of the equivalent circuit model using a multi-objective genetic algorithm. J. Power Sources. 2014;247:729–737.10.1016/j.jpowsour.2013.09.011Suche in Google Scholar

[25] Samadani E, Farhad S, Scott W, Mastali M, Gimenez LE, Fowler M, Fraser RA. Empirical modeling of lithium-ion batteries based on electrochemical impedance spectroscopy tests. Electrochim Acta. 2015;160:169–177.10.1016/j.electacta.2015.02.021Suche in Google Scholar

[26] Low W, Aziz J, Idris N, Saidur R. Electrical model to predict current– voltage behaviours of lithium ferro phosphate batteries using a transient response correction method. J Power Sources. 2013;221:201–209.10.1016/j.jpowsour.2012.07.140Suche in Google Scholar

[27] Salameh ZM, Casacca MA, Lynch WA. A mathematical model for lead-acid batteries. IEEE Trans Energy Convers. 1992;7(1):93–98.10.1109/60.124547Suche in Google Scholar

[28] Hentunen A, Lehmuspelto T, Suomela J. Time-domain parameter extraction method for th´evenin-equivalent circuit battery models. IEEE Trans Energy Convers. 2014;29(3):558–566.10.1109/TEC.2014.2318205Suche in Google Scholar

[29] Schweighofer B, Raab KM, Brasseur G. Modeling of high power automotive batteries by the use of an automated test system. IEEE Trans. Instrum. Meas. 2003;52(4):1087–1091.10.1109/TIM.2003.814827Suche in Google Scholar

[30] Pop V, Bergveld HJ, Danilov D, Regtien PP, Notten PH. Methods for measuring and modelling a battery’s electro-motive force. Battery Manage Syst: Accurate State-of-Charge Indication Battery-Powered Appl. 2008;63–94.Suche in Google Scholar

[31] Wang L, Zhao J, He X, Gao J, Li J, Wan C, Jiang C. Electrochemical impedance spectroscopy (eis) study of lini1/3co1/3mn1/3o2 for li-ion batteries. Int. J. Electrochem. Sci. 2012;7(1):345–353.Suche in Google Scholar

[32] Prada E, Domenico DD, Creff Y, Bernard J, Sauvant-Moynot V. A coupled od electrochemical ageing & electro-thermal li-ion modeling approach for hev/phev. In: Vehicle Power and Propulsion Conference (VPPC), 2011 IEEE, IEEE. 2011;1–8.10.1109/VPPC.2011.6043002Suche in Google Scholar

[33] Do DV, Forgez C. El Kadri Benkara K, Friedrich G. Impedance observer for a li-ion battery using kalman filter. IEEE Trans Veh Technol. 2009;58(8):3930–3937.10.1109/TVT.2009.2028572Suche in Google Scholar

[34] Rizoug N, Bartholomeus P, Le Moigne P. Modeling and characterizing supercapacitors using an online method. IRE Trans. Ind. Electron. 2010;57(12):3980–3990.10.1109/TIE.2010.2042418Suche in Google Scholar

[35] Chen Y-W, Chen J-S. A study of electrochemical performance of lifepo4/c composites doped with na and v. Int. J. Electrochem. Sci. 2012;7:8128–8139.Suche in Google Scholar

[36] Huria T, Ceraolo M, Gazzarri J, Jackey R. High fidelity electrical model with thermal dependence for characterization and simulation of high power lithium battery cells, in. Electric Vehicle Conference (IEVC), 2012 IEEE International, IEEE. 2012;1–8.10.1109/IEVC.2012.6183271Suche in Google Scholar

[37] Gandolfo D, Brand˜ao A, Pati˜no D, Molina M. Dynamic model of lithium polymer battery–load resistor method for electric parameters identification. J. Energy Inst. 2015;88(4):470–479.10.1016/j.joei.2014.10.004Suche in Google Scholar

[38] Dai H, Wei X, Sun Z, Wang J, Gu W. Online cell soc estimation of li-ion battery packs using a dual time-scale kalman filtering for ev applications. Appl. Energy. 2012;95:227–237.10.1016/j.apenergy.2012.02.044Suche in Google Scholar

[39] Lee Y-S, Cheng M-W. Intelligent control battery equalization for series connected lithium-ion battery strings. IEEE Trans. Ind. Electron. 2005;52(5):1297–1307.10.1109/TIE.2005.855673Suche in Google Scholar

[40] Kim T, Qiao A. A hybrid battery model capable of capturing dynamic circuit characteristics and nonlinear capacity effects. IEEE Trans Energy Convers. 2011;26(4):1172–1180.10.1109/TEC.2011.2167014Suche in Google Scholar

[41] Haifeng D, Xuezhe W, Zechang S. State and parameter estimation of a hev li-ion battery pack using adaptive kalman filter with a new soc-ocv concept. In: Measuring Technology and Mechatronics Automation, 2009. ICMTMA 2009. International Conference on, Vol. 2, IEEE 2009:375–380.Suche in Google Scholar

[42] Abu-Sharkh S, Doerffel D. Rapid test and non-linear model characterisation of solid-state lithium-ion batteries. J Power Sources. 2004;130(1):266–274.10.1016/j.jpowsour.2003.12.001Suche in Google Scholar

[43] Agarwal V, Uthaichana K, DeCarlo RA, Tsoukalas LH. Development and validation of a battery model useful for discharging and charging power control and lifetime estimation. IEEE Trans Energy Convers. 2010;25(3):821–835.10.1109/TEC.2010.2043106Suche in Google Scholar

[44] Wei X, Zhu B, Xu W. Internal resistance identification in vehicle power lithium-ion battery and application in lifetime evaluation. In: Measuring Technology and Mechatronics Automation, 2009. ICMTMA’09. International Conference on, Vol. 3, IEEE 2009:388–392.10.1109/ICMTMA.2009.468Suche in Google Scholar

[45] Schweiger H-G, Obeidi O, Komesker O, Raschke A, Schiemann M, Zehner C, Gehnen M, Keller M, Birke P. Comparison of several methods for determining the internal resistance of lithium ion cells. Sensors. 2010;10(6):5604–5625.10.3390/s100605604Suche in Google Scholar PubMed PubMed Central

[46] Devillers N, M-C P´era, Jemei S, Gustin F, Bienaim´e D. Complementary characterization methods for lithium-ion polymer secondary battery modelling. Int. J. Electr. Power Energy Syst. 2015;67:168–178.10.1016/j.ijepes.2014.11.020Suche in Google Scholar

[47] Lin X, Perez HE, Mohan S, Siegel JB, Stefanopoulou AG, Ding Y, Castanier MP. A lumped-parameter electro-thermal model for cylindrical batteries. J Power Sources. 2014;257:1–11.10.1016/j.jpowsour.2014.01.097Suche in Google Scholar

[48] Gong X, Mi CC. Temperature-dependent performance of lithium ion batteries in electric vehicles. In: Applied Power Electronics Conference and Exposition (APEC), 2015 IEEE, IEEE 2015:1065–1072.10.1109/APEC.2015.7104480Suche in Google Scholar

[49] Stroe D-I, Swierczynski M, Stroe A-I, Knap V, Teodorescu R, Andreasen SJ. Evaluation of different methods for measuring the impedance of lithiumion batteries during ageing. In: Ecological Vehicles and Renewable Energies (EVER), 2015 Tenth International Conference on, IEEE 2015:1–8.10.1109/EVER.2015.7113024Suche in Google Scholar