Hoeffding Evolutionary Algorithm and its Application in Building Equivalent Circuit Models for Lithium-Ion Battery

Article Preview

Abstract:

There has been a growing interest in building equivalent circuit models for lithium-ion battery with evolutionary algorithms. One of the well-known algorithm is the Hoeffding bound based evolutionary algorithm (HEA). In this paper, we first introduce the definitions of the Hoeffding bound, selection operations and Hoeffding evolutionary algorithm. And then, we introduced the method for building equivalent circuit models, which composed of four fundamental functions, connection-modifying function, component-creating function, arithmetic-performing functions and automatically defined function. In this way, a Hoeffding-evolutionary-algorithm based equivalent-circuit-model for lithium-ion battery is modeled. With the model-based state estimation approaches, the obtained model can assess the state-of-charge (SOC) of cells precisely.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1099-1102

Citation:

Online since:

January 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] L. Zhao, L. Wang: Engineering Applications of Artificial Intelligence, 25(2012), p.945.

Google Scholar

[2] E.G. Carrano, C.G. Taroco, O.M. Neto: Electrical Power and Energy Systems, 63(2014), p.645.

Google Scholar

[3] D.A. McAdams1, W. Li: Journal of Computing and Information Science in Engineering, 14(2014) , p.1.

Google Scholar

[4] C. Audet, J.J.E. Dennis, D.W. Moore, A. Booker, and P.D. Frank. in: Proc. AIAA/USAF/NASA/ ISSMO Symp. Multidisciplinary Anal. Opt., Paper No. AIAA-2000-4891, (2000).

DOI: 10.2514/6.1998-4717

Google Scholar

[5] J. Bartelemy, R.T. Haftka: Struct. Optim. 5(1993), p.129.

Google Scholar

[6] M. Birattari, T. Stutzle, L. Paquete, and K. Varrentrapp, in: Proceedings of Genetic and Evolutionary Computation Conference, 2002, p.11.

Google Scholar

[7] B. Bollobas: Random Struct. Algorithms, 18(2001), p.279.

Google Scholar

[8] J. Redmond, G. Parker: J. Optim. Theory Appl. 90 (1996), p.279.

Google Scholar

[9] W. Hoeffding: J. Am. Stat. Assoc. 58, (1963), p.13.

Google Scholar

[10] Idaho National Laboratory. Idaho Operation Office: Idaho Falls, ID, USA, (2010).

DOI: 10.2172/79044

Google Scholar