Abstract
Learning semi-physical fuzzy models of rechargeable Li-Ion batteries from data involves solving a complex multicriteria optimization task where the accuracies of the approximations of the different observable variables are balanced. The fitness function of this problem depends on the recursive evaluation of a set of differential equations, where fuzzy rule-based systems are embedded as nonlinear blocks. Evaluating this function is a time consuming process, thus algorithms that efficiently promote diversity and hence demand a low number of evaluations of the fitness function are preferred. In this paper, a comparison is carried out between some recent genetic algorithms, whose performances are assessed in this particular modelling problem. It is concluded that the combination of the recent \(\theta \)-Dominance Evolutionary Algorithm (\(\theta \)-DEA) with a Knowledge-based precedence operator, that improves the selection, is a sensible choice. Dominance relations between the Pareto fronts are assessed in terms of binary additive \(\epsilon \)-quality indicators.
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Notes
- 1.
Data publicly available at http://www.unioviedo.es/batterylab/.
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Acknowledgements
This work funded by the Eureka SD project (agreement number 2013-2591), that is supported by the Erasmus Mundus programme of the European Union. In addition, was supported by the Spanish Ministry of Science and Innovation (MICINN) and the Regional Ministry of the Principality of Asturias under Grants TIN2014-56967-R, DPI2013-46541-R and FC-15-GRUPIN14-073.
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Echevarría, Y., Sánchez, L., Blanco, C. (2016). Genetic Fuzzy Modelling of Li-Ion Batteries Through a Combination of Theta-DEA and Knowledge-Based Preference Ordering. In: Luaces , O., et al. Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science(), vol 9868. Springer, Cham. https://doi.org/10.1007/978-3-319-44636-3_29
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