@article {551,
title = {Using fuzzy preference orderings in theta-dominance with application to health monitoring of Li-ion batteries},
journal = {Journal of Multiple-Valued Logic and Soft Computing},
year = {2017},
author = {Echevarr{\'\i}a, Yuviny and Couso, In{\'e}s and Anse{\'a}n, David and Blanco, Cecilio and S{\'a}nchez, Luciano}
}
@conference {10.1007/978-3-319-32034-2_21,
title = {Assessment of Multi-Objective Optimization Algorithms for Parametric Identification of a Li-Ion Battery Model},
booktitle = {Hybrid Artificial Intelligent Systems},
year = {2016},
pages = {250{\textendash}260},
publisher = {Springer International Publishing},
organization = {Springer International Publishing},
address = {Cham},
abstract = {The identification of intelligent models of Li-Ion batteries is a major issue in Electrical Vehicular Technology. On the one hand, the fitness of such models depends on the recursive evaluation of a set of nonlinear differential equations over a representative path in the state space, which is a time consuming task. On the other hand, battery models are intrinsically unstable, and small differences in the initial state or the system, as well as imprecisions in the parameter values, may trigger large differences in the output. Hence, learning battery models from data is a complex multi-modal problem and the parameters of these models must be determined with a high accuracy. In addition to this, producing a dynamical model of a battery is a multi-criteria problem, because the predictive capabilities of the model must be estimated in both the voltage and the temperature domains. In this paper, a selection of state-of-the-art Multi-Objective Optimization Algorithms (SPEA2, NSGA-II, OMOPSO, NSGA-III and MOEA/D) are assessed with regard to their suitability for identifying a model of a Li-Ion battery. The dominance relations that occur between the Pareto fronts are discussed in terms of binary additive {\$}{\$}{\backslash}epsilon {\$}{\$}ϵ-quality indicators. It is concluded that each of the standard implementations of these algorithms has different issues with this particular problem, MOEA/D and NSGA-III being the best overall alternatives.},
isbn = {978-3-319-32034-2},
author = {Echevarr{\'\i}a, Yuviny and S{\'a}nchez, Luciano and Blanco, Cecilio},
editor = {Mart{\'\i}nez-{\'A}lvarez, Francisco and Troncoso, Alicia and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}
@conference {10.1007/978-3-319-44636-3_29,
title = {Genetic Fuzzy Modelling of Li-Ion Batteries Through a Combination of Theta-DEA and Knowledge-Based Preference Ordering},
booktitle = {Advances in Artificial Intelligence},
year = {2016},
pages = {310{\textendash}320},
publisher = {Springer International Publishing},
organization = {Springer International Publishing},
address = {Cham},
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 {\$}{\$}{\backslash}theta {\$}{\$}-Dominance Evolutionary Algorithm ({\$}{\$}{\backslash}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 {\$}{\$}{\backslash}epsilon {\$}{\$}-quality indicators.},
isbn = {978-3-319-44636-3},
author = {Echevarr{\'\i}a, Yuviny and S{\'a}nchez, Luciano and Blanco, Cecilio},
editor = {Luaces , Oscar and G{\'a}mez, Jos{\'e} A. and Barrenechea, Edurne and Troncoso, Alicia and Galar, Mikel and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}