Using Fuzzy Preference Orderings in theta-Dominance with Application to Health Monitoring of Li-Ion Batteries

Multiple-Valued Logic and Soft Computing(2019)

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Abstract
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 theta-Dominance Evolutionary Algorithm, that alters the prioritization of the individuals in the selection stage. An 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.
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Key words
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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