Learning human-understandable models for the health assessment of Li-ion batteries via Multi-Objective Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{ECHEVARRIA:2019:EAAI,
-
author = "Yuviny Echevarria and Cecilio Blanco and
Luciano Sanchez",
-
title = "Learning human-understandable models for the health
assessment of {Li}-ion batteries via Multi-Objective
Genetic Programming",
-
journal = "Engineering Applications of Artificial Intelligence",
-
year = "2019",
-
volume = "86",
-
pages = "1--10",
-
month = nov,
-
keywords = "genetic algorithms, genetic programming,
Multiobjective genetic programming, Grammatical
evolution, Battery model, Lithium",
-
ISSN = "0952-1976",
-
DOI = "doi:10.1016/j.engappai.2019.08.013",
-
URL = "http://www.sciencedirect.com/science/article/pii/S095219761930199X",
-
abstract = "The health of automotive Li-ion batteries depends on
different side reactions on the electrodes that may
degrade the cells, thereby reducing their useable
capacity and sometimes producing catastrophic failures
with serious economic and safety implications. In this
paper, a method of detection and prognosis of battery
deterioration is proposed in which an intelligent soft
sensor is able to synthesize human-understandable
health indicators from sequences of voltages, currents
and temperatures streamed via on-vehicle sensors. This
soft sensor is based on a dynamic model optimizing
three different criteria obtained by means of
multi-objective grammatical evolution. Different
survival selection strategies suitable for this problem
are discussed and compared",
- }
Genetic Programming entries for
Yuviny Echevarria Cartaya
Cecilio Jose Blanco Viejo
Luciano Sanchez
Citations