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Using Fuzzy Preference Orderings in θ-Dominance with Application to Health Monitoring of Li-Ion Batteries
Yuviny Echevarría, Inès Couso, David Anseán, Cecilio Blanco and Luciano Sánchez

A Genetic Fuzzy Model of the State of Health of a Li-Ion battery is developed where both outputs of the system and its first derivative with respect to the stored charge are approximated. This approximation is a viable diagnosis technique to detect cell degradation in modern Li-Ion battery technologies. The model is fitted to data by means of a specialization of the θ-Dominance Evolutionary Algorithm, that alters the prioritization of the individuals in the selection stage. A specific operator is used which complements Pareto Non-Dominance levels with a partial order at each level thus models that are potentially better have a reproductive advantage. An empirical study is performed where the results of different multi and many-objectives genetic algorithms are assessed for this problem.

Keywords: Genetic fuzzy systems, Li-ion battery model, multi-objective genetic algorithms, fuzzy preference orderings, battery state of charge, battery state of health

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