Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks

International Journal of Electrical Power & Energy Systems - Tập 42 Số 1 - Trang 487-494 - 2012
Akram Eddahech1, Olivier Briat1, Nicolas Bertrand1, Jean-Yves Delétage1, Jean-Michel Vinassa1
1UMR 5218 CNRS – IPB – Université Bordeaux 1, Laboratoire IMS, 351 Cours de la Libération, Bat A31, 33400 Talence, Bordeaux, France

Tóm tắt

Từ khóa


Tài liệu tham khảo

Du, 2011, Robustness of damping control implemented by energy storage systems installed in power systems, Int J Electr Power Energy Syst, 33, 35, 10.1016/j.ijepes.2010.08.006

Hajizadeh, 2010, Control of hybrid fuel cell/energy storage distributed generation system against voltage sag, Int J Electr Power Energy Syst, 32, 488, 10.1016/j.ijepes.2009.09.015

Roscher, 2012, High efficiency energy management in BEV applications, Int J Electr Power Energy Syst, 37, 126, 10.1016/j.ijepes.2011.10.022

Buller, 2003, Impedance based nonlinear dynamic battery modeling for automotive applications, J Power Sources, 113, 422, 10.1016/S0378-7753(02)00558-X

Liaw, 2004, Modeling of lithium ion cells-a simple equivalent-circuit model approach, Solid State Ionics, 175, 835, 10.1016/j.ssi.2004.09.049

Gao, 2002, Dynamic lithium-ion battery model for system simulation, IEEE Trans Compon Pack Technol, 25, 495, 10.1109/TCAPT.2002.803653

Andre, 2011, Characterization of high power lithium ion batteries by electrochemical impedance spectroscopy. II: Modeling, J Power Sources, 196, 5349, 10.1016/j.jpowsour.2010.07.071

Zhuang, 2010, An electrochemical impedance spectroscopic study of the electronic and ionic transport properties of spinel LiMn2O4, J Phys Chem, 114, 8614

Mandal LP, Cox RW. A transient-based approach to estimation of the electrical parameters of a lead-acid battery model. In: Proc IEEE energy conversion congress and exposition conf; 2010. p. 4238–42.

Remmlinger, 2011, State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation, J Power Sources, 196, 5357, 10.1016/j.jpowsour.2010.08.035

Eddahech A, Briat O, Chaari R, Bertrand N, Henry H, Vinassa J-M. Lithium-ion cell modeling from impedance spectroscopy for EV applications. In: Proc IEEE energy conversion congress and exposition conf, Phoenix, Arizona; 2011.

Moss, 2008, An electrical circuit for modeling the dynamic response of Li-ion polymer batteries, J Electrochem Soc, 155, A986, 10.1149/1.2999375

Hu, 2011, Electro-thermal battery model identification for automotive applications, J Power Sources, 196, 449, 10.1016/j.jpowsour.2010.06.037

Verbrugge, 2002, Electrochemical and thermal characterization of battery modules commensurate with electric vehicle integration, J Electrochem Soc, 149, 45, 10.1149/1.1426395

El Brouji, 2009, Impact of calendar life and cycling aging on supercapacitor performance, IEEE Trans Veh Technol, 8, 3917, 10.1109/TVT.2009.2028431

Tzirakis, 2006, Vehicle emissions and driving cycles: comparison of the Athens driving cycle (ADC) with ECE-15 and European driving cycle (EDC), Global NEST J, 8, 282

Zhang, 2011, State-of-charge estimation of valve regulated lead acid battery based on multi-state unscented Kalman filter, Int J Electr Power Energy Syst, 33, 472, 10.1016/j.ijepes.2010.10.010

Dursun, 2012, Comparative evaluation of different power management strategies of a stand-alone PV/Wind/PEMFC hybrid power system, Int J Electr Power Energy Syst, 34, 81, 10.1016/j.ijepes.2011.08.025

Weigert, 2011, State-of-charge prediction of batteries and battery–supercapacitor hybrids using artificial neural networks, J Power Sources, 196, 4061, 10.1016/j.jpowsour.2010.10.075

Singh, 2006, Design and implementation of a fuzzy logic-based state-of-charge meter for Li-ion batteries used in portable defibrillators, J Power Sources, 162, 829, 10.1016/j.jpowsour.2005.04.039

Smart, 2010, Life verification of large capacity Yardney Li-ion cells and batteries in support of NASA missions, Int J Energy Res, 34, 116, 10.1002/er.1653

Wohlfahrt-Mehrens, 2004, Aging mechanisms of lithium cathode materials, J Power Sources, 127, 58, 10.1016/j.jpowsour.2003.09.034

Broussely, 2005, Main aging mechanisms in Li ion batteries, J Power Sources, 146, 90, 10.1016/j.jpowsour.2005.03.172

Troltzsch, 2006, Characterizing aging effects of lithium ion batteries by impedance spectroscopy, Electrochim Acta, 51, 1664, 10.1016/j.electacta.2005.02.148

Eddahech, 2011, Aging monitoring of lithium-ion cell during power cycling tests, Microelectr Reliab J, 51, 1968, 10.1016/j.microrel.2011.07.013

Eddahech A, Briat O, Vinassa J-M. Neural networks based model and voltage control for lithium polymer batteries. In: 8th IEEE international symposium on diagnostics for electric machines. Power Electronics and Drives, Bologna; 2011.

Bonanno F, Capizzi G, Tina G. Long-term energy performance forecasting of integrated generation systems by recurrent neural networks. In: Proc IEEE clean elecrtical power conf, Italie; 2009. p. 673–8.

Basso, 2005, NARX models of an industrial power plant gas Turbine, IEEE Trans Control Syst Technol, 13, 599, 10.1109/TCST.2004.843129

Mazumdar, 2008, Recurrent neural networks trained with backpropagation through time algorithm to estimate nonlinear load harmonic currents, IEEE Trans Ind Electr, 55, 3484, 10.1109/TIE.2008.925315