Battery Model Parameters Estimation Using Simulated Annealing

Marcia de Fatima Brondani, Airam Teresa Zago Romcy Sausen, Paulo Sérgio Sausen, Manuel Osório Binelo

Abstract


In this paper, a Simulated Annealing (SA) algorithm is proposed for the Battery model parametrization, which is used for the mathematical modeling of the Lithium Ion Polymer (LiPo) batteries lifetime. Experimental data obtained by a testbed were used for model parametrization and validation. The proposed SA algorithm is compared to the traditional parametrization methodology that consists in the visual analysis of discharge curves, and from the results obtained, it is possible to see the model efficacy in batteries lifetime prediction, and the proposed SA algorithm efficiency in the parameters estimation.

Keywords


Battery model; parameter estimation; simulated annealing

Full Text:

PDF

References


J. Brand, Z. Zhang, R. K. Agarwal, Extraction of battery parameters of the equivalent circuit model using a multi-objective genetic algorithm, in Journal of Power Sources, 247, (2014), 729-737.

M. F. Brondani, A. Sausen, P. S. Sausen, M. O. Binelo, Modelagem Matemática do Tempo de Vida de Baterias de Lítio Íon Polímero, in “Proceeding Series of the Brazilian Society of Computational and Applied Mathematics”, 2016.

M. Ceylan, T. Sarikurt, A. Balikçi, A novel Lithium-Ion-Polymer battery model for hybrid/electric vehicles, in "2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE)", pp. 366-369, IEEE, 2014.

M. Chen, G. Rincón-Mora, Accurate electrical battery model capable of predicting runtime and I-V performance, in IEEE Transactions on Energy Conversion, 21, No. 2 (2006), 504-511.

Z. Chen, C. Chris Mi, B. Xia, C. You, Energy management of power-split plug-in hybrid electric vehicles based on simulated annealing and Pontryagin's minimum principle, in Journal of Power Sources, 272, (2014), 160-168.

M. Doyle, T. F. Fuller, J. S. Newman, Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell, in Journal of The Electrochemical Society, 140, (1993), 1526-1533.

K.M. El-Naggar, M.R. AlRashidi, M.F. AlHajri, A.K. Al-Othman, Simulated Annealing algorithm for photovoltaic parameters identication, in Solar Energy, 86, No. 1 (2012), 266-274.

O. Ekren, B. Y. Ekren, Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing, in Applied Energy, 87, No. 2 (2010), 592-598.

Implement generic battery model, http://www.mathworks.com/help/physmod/sps/powersys/ref/battery.html

T. Kim, W. Qiao, A hybrid battery model capable of capturing dynamic circuit characteristics an nonlinear capacity eects, in IEEE Wireless Communications and Networking Conference, 26, (2011), 1172-1180.

S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, Optimization by simulated annealing, in SCIENCE, 220, No. 4598 (1983), 671-680.

D. Linden, T. B. Reddy, Handbook of Batteries´´, McGraw-Hill Handbooks, New York, 1995.

N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, E. Teller, Equation of State Calculations by Fast Computing Machines, in Journal of Chemical Physics, 21, (1953), 1087-1092.

M.T. Outeiro, R. Chibante, A.S. Carvalho, A.T. de Almeida, A parameter optimized model of a Proton Exchange Membrane fuel cell including temperature effects, in Journal of Power Sources, 185, No. 2 (2008), 952-960.

T. D. Panigrahi, D. Panigrahi, C. Chiasserini, S. Dey, R. Rao, A. Raghunathan, K. Lahiri, Battery life estimation of mobile embedded systems, in "Fourteenth International Conference on VLSI Design", pp. 57-63, 2001.

C. M. D. Porciuncula, A. T. Z. R. Sausen, P. S. Sausen, Mathematical Modeling for Predicting Battery Lifetime through Electrical Models, in Advances in Mathematics Research´´, pp. 343-360, 2015.

D. Rakhmatov, S. Vrudhula, An analytical high-level battery model for use in energy management of portable electronic systems, in National Science Foundation's State/Industry/University Cooperative Research Centers (NSFS/IUCRC) Center for Low Power Electronics (CLPE), (2001), 1-6.

V. Ramadesigan, P. W. C. Northrop, S. De, S. Santhanagopalan, R. D. Braatz, V. R. Subramanian, Modeling and Simulation of Lithium-Ion Batteries from a Systems Engineering Perspective, in Journal of The Electrochemical Society, 159, (2012), 31-45.

K. Thirugnanam, J.T.P. Ezhil Reena, M. Singh, P. Kumar, Mathematical Modeling of Li-Ion Battery Using Genetic Algorithm Approach for V2G Applications, in IEEE Transactions on Energy Conversion, 29, No. 2 (2014), 332-343.

O. Tremblay, L.-A. Dessaint, A.-I. Dekkiche, A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles, in "Vehicle Power and Propulsion Conference, VPPC 2007", IEEE, pp. 284-289, 2007.

O. Tremblay, L-A. Dessaint, Experimental Validation of a Battery Dynamic Model for EV Applications, in World Electric Vehicle Journal, (2009), 289-298.

Z. Wang, B. Huang, Y. Xu, W. Li, Optimization of Series Hybrid Electric Vehicle Operational Parameters By Simulated Annealing Algorithm, in "2007 IEEE International Conference on Control and Automation", ICCA 2007, pp. 1536-1541, IEEE, 2007.




DOI: https://doi.org/10.5540/tema.2017.018.01.0127

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM

Refbacks

  • There are currently no refbacks.



Trends in Computational and Applied Mathematics

A publication of the Brazilian Society of Applied and Computational Mathematics (SBMAC)

 

Indexed in:

                       

         

 

Desenvolvido por:

Logomarca da Lepidus Tecnologia