Design of an efficient battery model using evolutionary algorithms.

S. Tamilselvi, N. Karuppiah, S. Muthubalaji

Abstract


Batteries play a vital role in current scenario of energy storage, even though many techniques of energy storage are available, since the time taken to start delivering the stored energy is very less. The battery life time depends upon its charging and discharging characteristics, which are in turn, depend on the internal parameters of battery. These parameters include resistance, capacitance and open circuit voltage. The amount of energy stored in the battery can be calculated by estimating these parameters. In this paper, an optimized model for Lithium ion batteries is presented using evolutionary algorithms to estimate the internal parameters of the battery over different charging and discharging rates. A sample EIG make, 2.5 V, 8 Ahr Lithium ion battery is modeled using two evolutionary algorithms such as genetic algorithm and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for different charging and discharging rates. The results of two algorithms are compared with the catalog values given by the manufacturer in order to identify the appropriate algorithm for battery modeling and validation. This paper concludes that battery characteristics obtained by CMA-ES algorithm match with the measured manufacturer characteristics.

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References


“Electricity Generation in India at a Glance”, http://en.wikipedia.org/wiki/Electricity sector in India http:// www. powermin.nic.in/ Indian electricity scenario/introduction.htm.

C. M. Shepherd, “Design of primary and secondary cells - part 2. an equation describing battery discharge,”JElectroche. Soc., vol. 112, pp. 657–664, Jul. 1965.

O. Tremblay, L.-A. Dessaint, and A.-I. Dekkiche, “A generic battery model for the dynamic simulation of hybrid electric vehicles,” IEEE Int. Vehicle Power and Propulsion Conf. (VPPC’07), pp. 284–289,Sept. 2007.

E. Sortomme and M. El-Sharkawi, “Optimal combined bidding of Vehicle-to-Grid ancillary services,”IEEE Trans. on Smart Grid, vol. 3, no. 1, pp. 70–79, Mar. 2012.

Praveen kumar, Pavol Bauser presented paper titled “Parameter extraction of Battery Models Using Multiobjective Optimization Genetic Algorithms” in 14 th International Power Electronics and motion Control Conference -2010.

“Mathematical Modeling of Li-Ion Battery Using Genetic Algorithm Approach for V2G Applications” , Kannan Thirugnanam, Student Member, IEEE, Ezhil Reena Joy T. P., Student Member, IEEE, Mukesh Singh, Student Member, IEEE, and Praveen Kumar, Member, IEEE-IEEE transactions on Energy conversions-2014 K. Deb, “Optimization for engineering design: Algorithms and examples,” Prentice Hall, India, 1998

Suganthan, P, Hansen, N, Liang, JJ, Deb, K, Chen, YP, Auger, A, & Tiwari, S 2005, 'Problem, definitions and evaluation Criteria for the CEC 2005', in Special Session on Real-Parameter Optimization, Technical report, Nanyang Technological University, Singapore.

Hansen, N, Auger, A, Ros, R, Finck, S, & Pošík, P 2010, 'Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009', Proceedings of the twelth annual conference companion on Genetic and evolutionary computation, GECCO, pp. 1689-1696.

Hansen, N, & Ostermeier, A 2001, 'Completely de randomized self-adaptation in evolution strategies', Evolutionary Computation, 9(2) 159-195.

Hansen, N 2006, 'The CMA evolution strategy: a comparing review', in Towards a new evolutionary computation, Springer Berlin, pp. 75-102.




DOI: http://dx.doi.org/10.21533/pen.v6i2.269

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Copyright (c) 2019 Tamilselvi S

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License