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350             Renewable Energy Devices and Systems with Simulations in MATLAB  and ANSYS ®
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              24.  S. Abu–Sharkh and D. Doerffel, Rapid test and non–linear model characterization of solid–state lithium–
                ion batteries, Journal of Power Sources, 130(1–2), 266–274, 2003.
              25.  L. Gao and S. Liu, Dynamic lithium–ion battery model for system simulation, IEEE Transactions on
                Components and Packaging Technologies, 25(3), 495–505, 2002.
              26.  A. Hamidi, L. Weber, and A. Nasiri, EV charging station integrating renewable energy and second–
                life battery, Proceedings of International Conference on Renewable Energy Research and Applications
                (ICRERA), pp. 1217–1221, Spain, 2013.
              27.  L. Zubieta and R. Bonert, Characterization of double-layer capacitor (DLCs) for power electronics appli-
                cation, IEEE Transactions on Industrial Applications, 36(1), 199–205, 2000.
              28.  S. Sivakkumar and A. Pandolfo, Evaluation of lithium-ion capacitors assembled with pre-lithiated graph-
                ite anode and activated carbon cathode, Electrochimica Acta, 65, 280–287, 2012.
              29.  E. Manla, G. Mandic, and A. Nasiri, Testing and modeling of lithium-ion ultracapacitors, Proceedings of
                Energy Conversion Congress and Exposition (ECCE), pp. 2957–2962, 2011.
              30.  G. Mandic and A. Nasiri, Modeling and simulation of a wind turbine system with ultracapacitors for
                short-term power smoothing, Proceedings of IEEE International Symposium on Industrial Electronics
                (ISIE), pp. 2431–2436, Italy, 2010.
              31.  N. Bertrand, O. Briat, J.-M. Vinassa, J. Sabatier, and H. El Brouji, Porous electrode theory for ultra-
                capacitor modelling and experimental validation, Proceedings of IEEE Vehicle Power and Propulsion
                Conference (VPPC), pp. 1–6, 2008.
              32.  N. Bertrand, J. Sabatier, O. Briat, and J. M. Vinassa, Embedded fractional nonlinear supercapacitor model and
                its parametric estimation method, IEEE Transactions on Industrial Electronics, 57(12), 3991–4000, 2010.
              33.  L. Shi and M. L. Crow, Comparison of ultracapacitor electric circuit models, Proceedings of IEEE PES
                General Meeting, pp. 1–6, 2008.
              34.  S. Buller, E. Karden, D. Kok, and R. W. De Doncker, Modeling the dynamic behavior of supercapacitors
                using impedance spectroscopy, IEEE Transactions on Industry Applications, 38(6), 1622–1626, 2002.
              35.  W. Yang, J. E. Carletta, T. T. Hartley, and R. J. Veillette, An ultracapacitor model derived using time-
                dependent current profiles, Proceedings of Midwest Circuit and Systems, MWSCAS, pp. 726–729, 2008.
              36.  A. Grama, L. Grama, D. Petreus, and C. Rusu, Supercapacitor modelling using experimental measure-
                ments, International Signals, Circuits and Systems, ISSCS, pp. 1–4, 2009.
              37.  J. N. Marie-Francoise, H. Gualous, and A. Berthon, Supercapacitor thermal and electrical behavior mod-
                eling using ANN, IEE Proceedings, Electric Power Applications, 153(2), 255–261, 2006.
              38.  D. Andrea, Battery Management Systems for Large Lithium Ion Battery Packs, 1st edn., Artech House,
                U.K., 2010.
              39.  Y. Xing, E. Ma, K. Tsui, and M. Pecht, Battery management systems in electric and hybrid vehicles,
                Energies, 4, 1840–1857, 2011.
              40.  K. Ng, C. Moo, Y. Chen, and Y. Hsieh, Enhanced coulomb counting method for estimating state-of-
                charge and state-of-health of lithium-ion batteries, Applied Energy, 86(9), 1506–1511, 2009.
              41.  J. Kozlowski, Electrochemical cell prognostics using online impedance measurements and model-based
                data fusion techniques, Proceedings of IEEE Aerospace Conference, 7, pp. 3257–3270, 2003.
              42.  A. Salkind, C. Fennie, and P. Singh, Determination of state-of-charge and state-of-health of batteries by
                fuzzy logic methodology, Journal of Power Sources, 80(1–2), 293–300, 1999.
              43.  N. Windarko, J. Choi, and G. Chung, SOC estimation of LiPB batteries using extended Kalman filter
                based on high accuracy electrical model, Proceedings of Power Electronics and ECCE Asia (ICPE &
                ECCE), pp. 2015–2022, Korea, 2011.
              44.  G. Plett, Extended kalman filtering for battery management systems of LiPB-based HEV battery packs
                part 2. State and parameter estimation, Journal of Power Sources, 134, 277–292, 2004.
              45.  R. Xiong, H. He, F. Sun, and K. Zhao, Evaluation on state of charge estimation of batteries with adap-
                tive extended Kalman filter by experiment approach, IEEE Transactions on Vehicular Technology, 62(1),
                108–117, 2013.
              46.  J. Yan, G. Xu, Y. Xu, and B. Xie, Battery state-of-charge estimation based on H∞ filter for hybrid electric
                vehicle, Proceedings of International Conference on Control, Automation, Robotics and Vision (ICARCV
                2008), pp. 464–469, Vietnam, 2008.
              47.  M. Gholizadeh and F. Salmasi, Estimation of state of charge, unknown nonlinearities, and state of health
                of a lithium-ion battery based on a comprehensive unobservable model, IEEE Transactions on Industrial
                Electronics, 61(3), 1335–1344, 2014.
              48.  T. Hansen and C. Wang, Support vector based battery state of charge estimator, Journal of Power Sources,
                141(2), 351–358, 2004.
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