- Hu Y., Yurkovich S., Guezennec Y., Yurkovich B. (2011) Electro-thermal battery model identification for automotive applications. J. Power Sources 196, 449-457. [CrossRef] [Google Scholar]
- Chaturvedi N., Klein R., Christensen J., Ahmed J., Kojic A. (2010) Algorithms for advanced battery-management systems, IEEE Control Syst. Mag. 30, 49-68. [CrossRef] [MathSciNet] [Google Scholar]
- Wang J., Guo J., Ding L. (2009) An adaptive Kalman filtering based state of charge combined estimator for electric vehicle battery pack, Energy Conver. Manage. 50, 3182-3186. [CrossRef] [Google Scholar]
- Piller S., Perrin M., Jossen A. (2001) Methods for state-of-charge determination and their applications, J. Power Sources 96, 113-120. [CrossRef] [Google Scholar]
- Lin C., Chen Q., Wang J. (2001) Improved Ah counting method for state of charge estimation of electric vehicle batteries, J. Tsinghua University Sci. Technol. 46, 247-251. [Google Scholar]
- Terry H., Wang C. (2005) Support vector based battery state of charge estimator, J. Power Sources 141, 351-358. [CrossRef] [Google Scholar]
- Barbarisi O., Vasca F., Glielmo L. (2006) State of charge Kalman filter estimator for automotive batteries, Control Eng. Pract. 14, 267-275. [CrossRef] [Google Scholar]
- Di Domenico D., Fiengo G., Stefanopoulou A. (2008) Lithium-ion battery state of charge estimation with a Kalman filter based on a electrochemical model, Proc. of 2008 IEEE International Conference on Control Applications, 3-5 Sept., 1, 702-707. [Google Scholar]
- Di Domenico D., Prada E., Creff Y. (2011) An adaptive strategy for Li-ion battery SOC estimation, Proceedings of 2011 IFAC World Congress, Milan, Italy, 28 Aug.-2 Sept., Vol. 3, Part. 1. [Google Scholar]
- Kleinand R., Chaturvedi N., Christensen J., Ahmed J., Findeisen R., Kojic A. (2010) State estimation of a reduced electrochemical model of a lithium-ion battery, Proceedings of 2010 American Control Conference (ACC 2010), Baltimore, Maryland, 30 June-2 July, pp. 6618-6623. [Google Scholar]
- Pang S., Farrell J., Du J., Barth M. (2001) Battery state-of-charge estimation, Proceedings of the American Control Conference (ACC 2001), Arlington, Virginia, 25–27 June, Vol. 2, 1644-1649. [Google Scholar]
- Plett G. (2004) Extended kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 1. Background, J. Power Sources 134, 252-261. [CrossRef] [Google Scholar]
- Plett G. (2004) Extended kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 2. Modeling and identification, J. Power Sources 134, 262-276. [CrossRef] [Google Scholar]
- Plett G. (2004) Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 3. State and parameter estimation, J. Power Sources 134, 277-292. [CrossRef] [Google Scholar]
- Pop V., Bergveld H.J., Op het Veld J.H.G., Regtien P.P.L., Danilov D., Notten P.H.L. (2006) Modeling battery behavior for accurate state-of-charge indication. J. Electrochem. Soc. 153, A2013-A2022. [CrossRef] [Google Scholar]
- Santhanagopalan S., White R. (2006) Online estimation of the state of charge of a lithium ion cell, J. Power Sources 161, 1346-1355. [CrossRef] [Google Scholar]
- Santhanagopalan S., White R. (2010) State of charge estimation using an unscented filter for high power lithium ion cells, Int. J. Energy Res. 34, 152-163. [CrossRef] [Google Scholar]
- Smith K., Rahn C., Wang C. (2008) Model-based electrochemical estimation of lithium-ion batteries, Proceedings of 2008 IEEE International Conference on Control Applications (CCA 2008), San Antonio, Texas, 3-5 Sept., pp. 714-719. [Google Scholar]
- Verbrugge M., Tate E. (2004) Adaptive state of charge algorithm for nickel metal hydride batteries including hysteresis phenomena, J. Power Sources 126, 236-249. [CrossRef] [Google Scholar]
- Kong Soon Ng, Chin-Sien Moo, Yi-Ping Chen, Yao-Ching Hsieh (2009) Enhanced Coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries, Appl. Energy 86, 1506-1511. [Google Scholar]
- Mauracher P., Karden E. (1997) Dynamic modelling of lead/acid batteries using impedance spectroscopy for parameter identification, J. Power Sources 67, 69-84. [CrossRef] [Google Scholar]
- Diard J.P., Le Gorrec B., Montella C. (1996) Cinétique électrochimique, Hermann, Paris. [Google Scholar]
- Cole K.S., Cole R.H. (1941) Dispersion and absorption in dielectrics I. alternating current characteristics, J. Chem. Phys. 9, 4, 341-351. [CrossRef] [Google Scholar]
- Andre D., Meilera M., Steinera K., Walza H., Soczka-Gutha T., Sauer D. (2011) Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. II. Modelling, J. Power Sources 196, 5349-5356. [CrossRef] [Google Scholar]
- Montaru M., Pelissier S. (2010) Frequency and temporal identification of a li-ion polymer battery model using fractional impedance, Oil Gas Sci. Technol. – Rev. IFP 65, 67-78. [CrossRef] [EDP Sciences] [Google Scholar]
- Kuhn E., Forgez C., Lagonotte P., Friedrich G. (2006) Modelling NiMH battery using Cauer and Foster structures. J. Power Sources 158, 1490-1497. [CrossRef] [Google Scholar]
- Gould C.R., Bingham C.M., Stone D.A., Bentley P. (2009) New battery model and state-of-health determination through subspace parameter estimation and state-observer techniques, IEEE Trans. Vehic. Technol. 58, 3905-3916. [CrossRef] [Google Scholar]
- Stuart T., Fang F., Wang X., Ashtiani C., Pesaran A. (2002) A modular battery management system for HEVs, Proceedings of the SAE Future Car Congress, SAE Technical Paper 2002-01-1918. [Google Scholar]
- Verbrugge M.W., Conell R.S. (2002) Electrochemical and thermal characterization of battery modules commensurate with electric vehicle integration, J. Electrochem. Soc. 149, 1, A45-A53. [CrossRef] [Google Scholar]
- Verbrugge M.W., Conell R.S. (2007) Electrochemical characterization of high-power lithium ion batteries using triangular voltage and current excitation sources, J. Power Sources 174, 2-8. [CrossRef] [Google Scholar]
- Hu T., Zanchi B., Zhao J. (2011) Simple analytical method for determining parameters of discharging batteries, IEEE Trans. Energy Conver. 26(3), 787-798. [CrossRef] [Google Scholar]
- Plett G. (2006) Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1: Introduction and state estimation, J. Power Sources 161, 1356-1368. [CrossRef] [Google Scholar]
- Plett G. (2005) Results of temperature-dependent LiPB cell modeling for HEV SOC estimation, Proceedings of the 21st Electric Vehicle Symposium, Monaco, 2-6 April. [Google Scholar]
- Newman J., Tiedemann W. (1975) Porous-electrode theory with battery applications, AIChE J. 21, 25-41. [Google Scholar]
- Wang C., Gu W., Liaw B. (1998) Micro-macroscopic coupled modeling of batteries and fuel cells. Part I. Model development, J. Electrochem. Soc. 145, 3407-3417. [CrossRef] [Google Scholar]
- Wang C., Gu W., Liaw B. (1998) Micro-macroscopic coupled modeling of batteries and fuel cells. Part II. Application to nickel-cadmium and nickel-metal hydride cells, J. Electrochem. Soc. 145, 3418-3427. [CrossRef] [Google Scholar]
- Gu W., Wang C. (2000) Thermal and electrochemical coupled modeling of a lithium-ion cell, Proc. ECS 99-25, 748-762. [Google Scholar]
- Smith K., Wang C. (2006) Solid-state diffusion limitations on pulse operation of a lithium-ion cell for hybrid electric vehicles, J. Power Sources 161, 628-639. [CrossRef] [Google Scholar]
- Ramadass P., Haran B., Gomadam P., White R., Popov B. (2004) Development of first principles capacity fade model for li-ion cells, J. Electrochem. Soc. 151, A196-A203. [Google Scholar]
- Weidner J.W., Timmerman P. (1994) Effect of proton diffusion, electron conductivity, and charge-transfer resistance on nickel hydroxide discharge curves, J. Electrochem. Soc. 141, 346-351. [CrossRef] [Google Scholar]
- Haran B., Popov B., White R. (1998) Determination of the hydrogen diffusion coefficient in metal hydrides by impedance spectroscopy. J. Power Sources 75, 56-63. [CrossRef] [Google Scholar]
- Di Domenico D., Stefanopoulou A., Fiengo G. (2010) Lithium-ion battery state of charge and critical surface charge estimation using an electrochemical model-based extended kalman filter, J. Dyn. Syst. Meas. Control 132, 6, 61302-61313. [CrossRef] [Google Scholar]
- Santhanagopalan S., Guo Q., Ramadass P., White R. (2006) Review of models for predicting the cycling performance of lithium-ion batteries, J. Power Sources 156, 620-628. [CrossRef] [Google Scholar]
- Jacobsen T., West K. (1995) Diffusion impedance in planar, cylindrical and spherical symmetry, Electrochimica Acta 40, 2, 255-262, ISSN 0013-4686. [CrossRef] [Google Scholar]
- Bernard J., Sciarretta A., Touzani Y., Sauvant-Moynot V. (2010) Advances in electrochemical models for predicting the cycling performance of traction batteries: Experimental study on NiMH and simulation, Oil Gas Sci. Technol. – Rev. IFP 65, 55-66. [CrossRef] [EDP Sciences] [Google Scholar]
- Wu B., Mohammed M., Brigham D., Elder R., White R. (2001) A non-isothermal model of a nickel-metal hydride cell, J. Power Sources 101, 149-157. [CrossRef] [Google Scholar]
- Siegel J.B., Lin X., Stefanopoulou A., Hussey D.S., Jacobson D.L., Gorsich D. (2011) Neutron imaging of lithium concentration in LFP pouch cell battery, J. Electrochem. Soc. 158, A523-A529. [CrossRef] [Google Scholar]
- Oustaloup A., Cois O., Le Lay L. (2005) Représentation et identification par modèle non entier, Lavoisier, Paris. [Google Scholar]
- Sabatier J., Aoun M., Oustaloup A., Grègoire G., Ragot F., Roy P. (2006) Fractional system identification for lead acid battery state of charge estimation, Signal Process 86, 2645-2657. [CrossRef] [Google Scholar]
Issue |
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 68, Number 1, January-February 2013
IFP Energies nouvelles International Conference: RHEVE 2011: International Conference on Hybrid and Electric Vehicles
|
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Page(s) | 127 - 135 | |
DOI | https://doi.org/10.2516/ogst/2012072 | |
Published online | 26 February 2013 |
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