Mathematical Modeling and Parameter Estimation of Battery Lifetime using a Combined Electrical Model and a Genetic Algorithm

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

Abstract


In this paper, a parametrization methodology based on the Genetic Algorithm meta-heuristic is proposed for the Chen and Rincón-Mora model parameter estimation, this model is used for the mathematical modeling of the Lithium-ion Polymer batteries lifetime. The model is also parametrized using the conventional procedures, which is based on the visual analysis of pulsed discharge curves, as presented in the literature. For both parametrization procedures, and for the model validation, experimental data obtained from a platform test are used. The results show that the proposed Genetic Algorithm is able to parametrize the model with better efficacy, presenting lower mean error, and also is a more agile process than the conventional one, been less subject to subjective aspects.

Keywords


Parameter estimation, Genetic algorithm meta-heuristic, Mathematical modeling

Full Text:

PDF

References


W. H. Meyer, Polymer Electrolytes for Lithium-Ion Batteries, Advanced Materials, vol. 10, pp. 439-448, 1998.

S. Manzetti and F. Mariasiu, Electric vehicle battery technologies: From present state to future systems, Renewable and Sustainable Energy Reviews, vol. 51, pp. 1004-1012, 2015.

G. P. Hammond and T. Hazeldine, Indicative energy technology assessment of advanced rechargeable batteries, Applied Energy, vol. 138, pp. 559 -571, 2015.

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

B. Suthar, V. Ramadesigan, P. W. C. Northrop, R. B. Gopaluni, S. Santhanagopalan, R. D. Braatz, and V. R. Subramanian, Optimal control and state estimation of lithium-ion batteries using reformulated models, in American Control Conference, ACC 2013, Washington, DC, USA, June 17-19, 2013, pp. 5350-5355, 2013.

C. Chiasserini and R. Rao, Pulsed battery discharge in communication devices, Proceedings of the 5th International Conference on Mobile Computing and Networking, pp. 88-95, 1999.

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

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

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

M. Chen and G. Rincón-Mora, Accurate electrical battery model capable of predicting runtime and i-v performance, IEEE Transactions on Energy Conversion, vol. 21, pp. 504-511, June 2006.

T. Hu and H. Jung, Simple algorithms for determining parameters of circuit models for charging/discharging batteries, Journal of Power Sources, vol. 233, pp. 14-22, 2013.

L. C. Romio, A. T. Z. R. Sausen, P. S. Sausen, and M. Reimbold, Mathematical Modeling of the Lithium-ion battery Lifetime using System Identication Theory. Advances in Mathematics Research, Nova Science Publishers Incorporated, 2015.

M. F. Brondani, A. Sausen, P. S. Sausen, and M. O. Binelo, Battery model parameters estimation using simulated annealing, TEMA - Tendências em Matemática Aplicada e Computacional, vol. 18, pp. 127-135, 2017.

M. F. Brondani, A. T. Z. R. Sausen, P. S. Sausen, and M. O. Binelo, Parameter estimation of lithium ion polymer battery mathematical model using genetic algorithm, Computational and Applied Mathematics, pp. 1-18, 2017.

U. Sahapatsombut, H. Cheng, and K. Scott, Modelling of electrolyte degradation and cycling behaviour in a lithiumair battery, Journal of Power Sources, vol. 243, pp. 409-418, 2013.

B. R. Pattipati, C. Sankavaram, and K. R. Pattipati, System identication and estimation framework for pivotal automotive battery management system characteristics, IEEE Trans. Systems, Man, and Cybernetics, Part C, vol. 41, no. 6, pp. 869-884, 2011.

A. Fotouhi, D. J. Auger, K. Propp, S. Longo, and M. Wild, A review on electric vehicle battery modelling: From lithium-ion toward lithiumsulphur, Renewable and Sustainable Energy Reviews, vol. 56, pp. 1008-1021, 2016.

H. He, R. Xiong, H. Guo, and S. Li, Comparison study on the battery models used for the energy management of batteries in electric vehicles, Energy Conversion and Management, vol. 64, pp. 113-121, 2012. {IREC} 2011, The International Renewable Energy Congress.

S. M. Rezvanizaniani, Z. Liu, Y. Chen, and J. Lee, Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (ev) safety and mobility, Journal of Power Sources, vol. 256, pp. 110-124, 2014.

T. Luo, L. Li, V. Ghorband, Y. Zhan, H. Song, and J. B. Christen, A portable impedance-based electrochemical measurement device, in 2016 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 2891-2894, May 2016.

A. Seaman, T.-S. Dao, and J. McPhee, A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation, Journal of Power Sources, vol. 256, pp. 410-423, 2014.

C. Lin, H. Mu, R. Xiong, and W. Shen, A novel multi-model probability battery state of charge estimation approach for electric vehicles using h-innity algorithm, Applied Energy, vol. 166, pp. 76-83, 2016.

H. He, R. Xiong, X. Zhang, F. Sun, and J. Fan, State-of-charge estimation of the lithium-ion battery using an adaptive extended kalman lter based on an improved thevenin model, IEEE Transactions on Vehicular Technology, vol. 60, pp. 1461-1469, May 2011.

S. E. Li, B. Wang, H. Peng, and X. Hu, An electrochemistry-based impedance model for lithium-ion batteries, Journal of Power Sources, vol. 258, pp. 9-18, 2014.

S. Mousavi and M. Nikdel, Various battery models for various simulation studies and applications, Renewable and Sustainable Energy Reviews, vol. 32, pp. 477-485, 2014.

C. Zhang, K. Li, S. Mcloone, and Z. Yang, Battery modelling methods for electric vehicles - a review, in 2014 European Control Conference (ECC), pp. 2673-2678, June 2014.

O. Tremblay, L.-A. Dessaint, and A.-I. Dekkiche, A generic battery model for the dynamic simulation of hybrid electric vehicles, in Vehicle Power and Propulsion Conference, 2007. VPPC 2007. IEEE, pp. 284-289, Sept 2007.

M. F. B. Binelo, L. B. Motyczka, A. T. Z. R. Sausen, P. S. Sausen, and M. O. Binelo, Battery Charge and Discharge Behavior Prediction Using Electrical Mathematical Models. Advances in Mathematics Research, Nova Science Publishers Incorporated, 2017.

J. E. B. Randles, Kinetics of rapid electrode reactions, Discuss. Faraday Soc., vol. 1, pp. 11-19, 1947.

A. Malik, Z. Zhang, and R. K. Agarwal, Extraction of battery parameters using a multi-objective genetic algorithm with a non-linear circuit model, Journal of Power Sources, vol. 259, pp. 76-86, 2014.

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

V. Sangwan, A. Sharma, R. Kumar, and A. K. Rathore, Estimation of battery parameters of the equivalent circuit models using meta-heuristic techniques, in 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), IEEE, pp. 1-6, July 2016.

K. Thirugnanam, J. Ezhil Reena, M. Singh, and P. Kumar, Mathematical modeling of li-ion battery using genetic algorithm approach for v2g applications, Energy Conversion, IEEE Transactions on, vol. 29, pp. 332-343, June 2014.

J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor, MI, USA: University of Michigan Press, 1975.

C. Darwin, On the origin of species by means of natural selection; or, The preservation of favoured races in the struggle for life. D. Appleton and Company, 1861.

D. Linden and T. B. Reddy, Handbook of Bateries. McGraw-Hill Handbooks, 3 ed ed., 1995.

B. Schweighofer, K. M. Raab, and G. Brasseur, Modeling of high power automotive batteries by the use of an automated test system, IEEE Transactions on Instrumentation and Measurement, vol. 52, pp. 1087-1091, Aug 2003.




DOI: https://doi.org/10.5540/tema.2019.020.01.149

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM

Refbacks

  • There are currently no refbacks.



TEMA - Trends in Applied and Computational Mathematics

A publication of the Brazilian Society of Applied and Computational Mathematics (SBMAC)
ISSN: 1677-1966  (print version),  2179-8451  (online version)

Indexed in:

                        

 

Desenvolvido por:

Logomarca da Lepidus Tecnologia