Review on Lithium-Ion battery modeling for different applications

Jaouad Khalfi; Najib Boumaaz; Abdallah  Soulmani

Authors

Jaouad Khalfi Department of Physics, Laboratory of Electrical Systems, Energy Efficiency and Telecommunications, Faculty of Sciences and Technology, Cadi Ayyad University, Marrakech, Morocco
Najib Boumaaz Department of Physics, Laboratory of Electrical Systems, Energy Efficiency and Telecommunications, Faculty of Sciences and Technology, Cadi Ayyad University, Marrakech, Morocco
Abdallah Soulmani Department of Physics, Laboratory of Electrical Systems, Energy Efficiency and Telecommunications, Faculty of Sciences and Technology, Cadi Ayyad University, Marrakech, Morocco

Keywords:

Battery modeling, lithium-ion battery, Electrochemical models,, Analytical models, equivalent electrical circuit model, fractional order model

Abstract

Battery modeling is one of the most important functions in a battery management system for different applications such as electrical vehicles, This article focuses on state of the art of lithium-ion battery modeling by exploring different existing modeling methods, such as Electrochemical models, Analytical models and the equivalent electrical circuit. First, the characteristics of the lithium-ion battery for different applications are reviewed ,we chose to study this type of battery because it offers satisfactory characteristics compared to other battery types, then the different modeling methods have been explored, finaly a conclusion with suggestion of other modeling type such as fractional order model have been proposed to improve efficiency and precision of battery management system.

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References

Lai X, Gao W, Zheng Y et al (2019) A comparative study of global optimization methods for parameter identifcation of different equivalent circuit models for Li-ion batteries. Electrochim Acta 295:1057–1066 2.

Song Z, Hofmann H, Lin X et al (2018) Parameter identifcation of lithium-ion battery pack for diferent applications based on Cramer-Rao bound analysis and experimental study. Appl Energy 231:1307–1318

Ali E (2014) Advanced electric drive vehicles. CRC Press, Boca Roton 4.

Chemali E, Preindl M, Malysz P et al (2016) Electrochemical and electrostatic energy storage and management systems for electric drive vehicles: State-of-the-art review and future trends. IEEE J Emerg Select Topics in Power Electron 4(3):1117–1134 5.

Lu L, Han X, Li J et al (2013) A review on the key issues for lithium-ion battery management in electric vehicles. J Power Sources 226:272–288

Dedryvere R, Foix D, Franger S et al (2010) Electrode/electrolyte interface reactivity in high-voltage spinel LiMn1. 6Ni0. 4O4/Li4Ti5O12 lithium-ion battery. J Phys Chem C 114(24):10999–11008

TRAN, Manh-Kien, MEVAWALA, Anosh, PANCHAL, Satyam, et al. Effect of integrating the hysteresis component to the equivalent circuit model of Lithium-ion battery for dynamic and non-dynamic applications. Journal of Energy Storage, 2020, vol. 32, p. 101785.

BACCOUCHE, Ines, MANAI, Bilal, et AMARA, Najoua Essoukri Ben. SoC estimation of LFP Battery Based on EKF Observer and a Full Polynomial Parameters-Model. In: 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). IEEE, 2020. p. 1-5.

S. Panchal, I. Dincer, M. Agelin-Chaab, M. Fowler, and R. Fraser, “Uneven temperature and voltage distributions due to rapid discharge rates and different boundary conditions for series-connected LiFePO 4 batteries,” Int. Commun. Heat Mass Transf., vol. 81, pp. 210–217, Feb. 2017.

S. Panchal et al., “Cycling degradation testing and analysis of a LiFePO 4 battery at actual conditions,” Int. J. Energy Res., vol. 41, no. 15, pp. 2565–2575, Dec. 2017.

B. Xia et al., “Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter,” Energies, vol. 11, no. 1, p. 3, Dec. 2017.

Z. Li, R. Xiong, and H. He, “An Improved Battery On-line Parameter Identification and State-of-charge Determining Method,” Energy Procedia, vol. 103, pp. 381–386, Dec. 2016.

ZAZI, Malika, et al. Nonlinear black box modeling of a lead acid battery using Hammerstein-Wiener model. Journal of Theoretical and Applied Information Technology, 2016, vol. 89, no 2, p. 476.

HOW, Dickshon NT, HANNAN, Mahammad A., LIPU, Molla S. Hossain, et al. State-of-charge estimation of Li-ion battery in electric vehicles: A deep neural network approach. IEEE Transactions on Industry Applications, 2020, vol. 56, no 5, p. 5565-5574.

E. M. Laadissi, A. E. Filali, and M. Zazi, “A Nonlinear TSNN Based Model of a Lead Acid Battery,” Bulletin of Electrical Engineering and Informatics, vol. 7, no. 2, p. 169-175, 2018.

LI, Weihan, FAN, Yue, RINGBECK, Florian, et al. Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter. Journal of Power Sources, 2020, vol. 476, p. 228534

LI, Weihan, CAO, Decheng, JÖST, Dominik, et al. Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries. Applied Energy, 2020, vol. 269, p. 115104.

URBAIN, Matthieu. Modélisation électrique et énergétique des accumulateurs Li-Ion. Estimation en ligne de la SOC et de la SOH. 2009. Thèse de doctorat. Institut National Polytechnique de Lorraine

Miomandre, F., Sadki, S., Audebert, P., and Méallet-Renault, R. (2011). Électrochimie - 2e éd. : Des concepts aux applications. Dunod. GoogleBooks-ID: zis0_5G_x4Sc

EDDAHECH, Akram. Modélisation du vieillissement et détermination de l’état de santé de batteries lithium-ion pour application véhicule électrique et hybride. 2013. Thèse de doctorat.

MAYÉ, Pierre. Générateurs électrochimiques : Piles, accumulateurs et piles à combustible. Dunod, 2010

Ianniciello, L.; Biwolé, P.H.; Achard, P. Electric vehicles batteries thermal management systems employing phase change materials. J. Power Sources 2018, 378, 383–403.

Wang, Q.; Jiang, B.; Li, B.; Yan, Y. A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles. Renew. Sustain. Energy Rev. 2016, 64,106–128.

M. Jongerden and B. Haverkort, “Which battery model to use?,” Software, IET, vol. 3,pp. 445–457, Dec. 2009

A. Jokar, B. Rajabloo, M. Désilets, and M. Lacroix, “An inverse method for estimating the electrochemical parameters of lithium-ion batteries: I. methodology,” Journal of The Electrochemical Society, vol. 163, no. 14, pp. A2876–A2886, 2016.

M. Safari and C. Delacourt, “Simulation-based analysis of aging phenomena in a commercial graphite/lifepo4 cell,” Journal of The Electrochemical Society, vol. 158, no. 12, pp. A1436–A1447, 2011.

M. Farkhondeh and C. Delacourt, “Mathematical modeling of commercial lifepo4 electrodes based on variable solid-state di?usivity,” Journal of The Electrochemical Society, vol. 159, no. 2, pp. A177–A192, 2011.

M. Farkhondeh, M. Safari, M. Pritzker, M. Fowler, T. Han, J. Wang, and C. Delacourt, “Full-range simulation of a commercial lifepo4 electrode accounting for bulk and surface e?ects: A comparative analysis,” Journal of The Electrochemical Society, vol. 161, no. 3, pp. A201–A212, 2014.

J. Newman and W. Tiedemann, “Potential and current distribution in electrochemical cells: Interpretation of the half-cell voltage measurements as a function of reference-electrode location,” Journal of The Electrochemical Society, vol. 140, no. 7, pp. 1961–1968, 1993.

M. Doyle and J. Newman, “The use of mathematical modeling in the design of lithium/polymer battery systems,” Electrochimica Acta, vol. 40, no. 13-14, pp. 2191–2196,1995. International symposium on polymer electrolytes.

K. Lee, G. Kim, and K. Smith, “3d thermal and electrochemical model for spirally wound large format lithium-ion batteries (presentation),” tech. rep., National Renewable Energy Laboratory (NREL), Golden, CO., 2010.

M. Doyle, J. Newman, A. S. Gozdz, C. N. Schmutz, and J.-M. Tarascon, “Comparison of modeling predictions with experimental data from plastic lithium ion cells,” Journal of The Electrochemical Society, vol. 143, no. 6, pp. 1890–1903, 1996.

A. Jokar, B. Rajabloo, M. Désilets, and M. Lacroix, “An inverse method for estimating the electrochemical parameters of lithium-ion batteries: I. methodology,” Journal of The Electrochemical Society, vol. 163, no. 14, pp. A2876–A2886, 2016.

J. Manwell and J. McGowan, ““lead acid battery storage model for hybrid energy systems,”Solar Energy, vol. 50, pp. 399–405, 1993.

J. M. et al., “Evaluation of battery models for wind/hybrid power system simulation,” in in Proceedings of the 5th European Wind Energy Association Conference (EWEC ’94), pp. 1182–1187, 1994.

D. Rakhmatov and S. Vrudhula, “An analytical high-level battery model for use in energy management of portable electronic systems,” in Proc. 2001 IEEE/ACM Int’l Conf. Computer-Aided Design (I. Press, ed.), pp. 488–493, 2001.

M. Jongerden and B. Haverkort, “Which battery model to use ?,” Software, IET, vol. 3, pp. 445–457, Dec. 2009.

Lin, C., Mu, H., Xiong, R., and Shen, W. (2016). A novel multi-model probability battery state of charge estimation approach for electric vehicles using Hinfinity algorithm. Applied Energy, 166(Supplement C) :76–83.

Plett, G. L. (2004b). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification. Journal of Power Sources, 134(2): 262–276.