@conference {7737717,
	title = {Battery diagnosis for electrical vehicles through semi-physical fuzzy models},
	booktitle = {2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2016},
	month = {July},
	pages = {416-423},
	abstract = {An intelligent model of a Li-Ion battery for electrical vehicles is proposed that allows for a fast battery health evaluation without the need of removing the battery from the vehicle. The only data needed for performing the condition monitoring are logged performance records (currents and voltages) that are commonly available at Battery Management Systems. The model comprises a combination of differential equations and fuzzy rule-based systems, these last being embedded as non-linear blocks in the differential equations. Fuzzy rules are learnt from data with the help of metaheuristics and fine tuned with gradient descent algorithms. A TensorFlow{\texttrademark} implementation of the learning algorithm has been developed that takes advantage of the numerical processing capabilities of a massively parallel GPU and improves the learning speed by a high margin.},
	keywords = {Batteries, battery diagnosis, battery health evaluation, battery management systems, Battery models, battery powered vehicles, Computational modeling, differential equations, Discharges (electric), electrical vehicles, fuzzy models, Fuzzy rule-based systems, Fuzzy systems, gradient descent algorithms, graphics processing units, learning (artificial intelligence), learning algorithm, Li-FePO4batteries, Li-ion battery, Liquids, massively parallel GPU, Mathematical model, nonlinear blocks, Numerical models, parallel processing, power engineering computing, secondary cells, semiphysical fuzzy models, TensorFlow, TensorFlow{\texttrademark}, Vehicles},
	doi = {10.1109/FUZZ-IEEE.2016.7737717},
	author = {L. S{\'a}nchez and J. Otero and I. Couso and C. Blanco}
}
@conference {4295659,
	title = {Learning Fuzzy Linguistic Models from Low Quality Data by Genetic Algorithms},
	booktitle = {2007 IEEE International Fuzzy Systems Conference},
	year = {2007},
	month = {July},
	pages = {1-6},
	abstract = {Incremental rule base learning techniques can be used to learn models and classifiers from interval or fuzzy-valued data. These algorithms are efficient when the observation error is small. This paper is about datasets with medium to high discrepancies between the observed and the actual values of the variables, such as those containing missing values and coarsely discretized data. We will show that the quality of the iterative learning degrades in this kind of problems, and that it does not make full use of all the available information. As an alternative, we propose a new implementation of a mutiobjective Michigan-like algorithm, where each individual in the population codifies one rule and the individuals in the Pareto front form the knowledge base.},
	keywords = {Degradation, Fuzzy sets, Fuzzy systems, genetic algorithms, Global Positioning System, incremental rule base learning techniques, Iterative algorithms, iterative learning degrades, knowledge based systems, learning (artificial intelligence), learning fuzzy linguistic models, Low Quality Data, Noise measurement, Pareto front form, Pareto optimisation, Position measurement, Stochastic resonance, Uncertainty},
	issn = {1098-7584},
	doi = {10.1109/FUZZY.2007.4295659},
	author = {L. S{\'a}nchez and J. Otero}
}
