Aportes a los Algoritmos de Aprendizaje Multiobjetivo para Modelos Semi-fisicos de Estimacion del Estado de Salud en Baterias

Created by W.Langdon from gp-bibliography.bib Revision:1.8098

@PhdThesis{Echevarria-Cartaya:thesis,

• author = "Yuviny {Echevarria Cartaya}",
• title = "Aportes a los Algoritmos de Aprendizaje Multiobjetivo para Modelos Semi-fisicos de Estimacion del Estado de Salud en Baterias",
• school = "Departamento de Informatica, Universidad de Oviedo",
• year = "2017",
• address = "Spain",
• month = jul,
• keywords = "genetic algorithms, genetic programming, Lithium Ion batteries",
• URL = "http://hdl.handle.net/10651/45012",
• URL = "http://digibuo.uniovi.es/dspace/bitstream/10651/45012/1/TD_YuvinyEchevarria.pdf",
• size = "195 pages",
• abstract = "Increasing the use of renewable energy sources reducing emissions of polluting gases to the environment is essential for achieving sustainable development. Nowadays, one of the main goals to reduce the world pollution is related to the control of carbon dioxide emissions produced by conventional engine automobiles. Electric vehicles are a good alternative to mitigate this environmental pollution problem. The efficiency of the electric vehicles, that use Li-Ion batteries,grows with the scientific innovation. Optimizing Li-Ion battery operation is not a simple task. Li-Ion batteries for automotive applications are complex and unstable dynamical systems with multiple inputs and outputs. Estimating the State of Health of the Li-Ion batteries is the major challenge for the developing of battery model. For this reason, developing of accurate estimation techniques, for battery management systems in electric vehicle, requires the concentration of the scientific community. This thesis proposes a new generation of dynamical models for the diagnosis of the State of Health in Li-Ion batteries. The models are based on partial knowledge of the electrochemical and thermodynamic phenomena defining the behavior of a Li-Ion battery.

  The semi-physical models comprise a set of differential equations with intelligent elements embedded that minimize the number of small black boxes. The learning process of the resulting Multiobjective Genetic Fuzzy Systems requires powerful algorithms. Due to the necessary approximation of the first derivative of the battery voltage respect to the stored charge. This is an expensive procedure and small changes in the voltage curve cause large excursion of the first derivative. The fitness evaluation in each generation is more than the ninety percent of the consumed time. On the other hand, existing evolutionary learning processes generate a high number of dominance-resistant individuals. All this motivates two major contributions made in this thesis.

  The first contribution is the knowledge injection through fuzzy preference order in to the learning process. Thus, prioritization of the individuals is altered in the survival selection stage. A tailored-made operator is used which complements Pareto Non-Dominance levels with a partial order at each level. The learned models are potentially better for the advantages of the proposed evolutive pressure mechanism. It has been shown, that accurate State of Health models for Li-Ion batteries can be obtained if a knowledge-based preference ordering of individuals is implemented. In this work, an empirical study is performed and the result of different multi and many-objectives genetic algorithms are assessed.

  The second contribution is focused in the learning process of a simple semi-physical model for the State of Health estimation. This model is based on the side reactions on the electrodes that can degrade a battery. In this case, the learning process requires the indirect estimation of a latent variable with human understandable structure. The contribution extends the Multiobjective Genetic Programming-Based Learning by using different survival selection strategies suitable for this problem. The proposed algorithm Grab-MO-GaP incorporates recent advances developed for many-objectives genetic algorithms. The proposed algorithm uses Grammatical Evolution to enforce the monotonicity of the latent variable respect to the model outputs and works as an evolutive pressure mechanism. The human-readable structures allow obtaining the location of the characteristic points of the negative electrode when the battery is being charged or discharged at a low current",

• notes = "In Spanish

  Supervisor: Luciano Sanchez Ramos",

}

Genetic Programming entries for Yuviny Echevarria Cartaya

Citations