An Analytical Model for Lithium-Ion Batteries Based on Genetic Programming Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8178
- @InProceedings{Milano:2023:MetroAutomotive,
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author = "F. Milano and G. {Di Capua} and N. Oliva and
F. Porpora and C. Bourelly and L. Ferrigno and
M. Laracca",
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booktitle = "2023 IEEE International Workshop on Metrology for
Automotive (MetroAutomotive)",
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title = "An Analytical Model for Lithium-Ion Batteries Based on
Genetic Programming Approach",
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year = "2023",
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month = jun,
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pages = "35--40",
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keywords = "genetic algorithms, genetic programming, Analytical
models, Voltage, Mathematical models, Batteries,
Behavioural sciences, Titanium compounds, Batteries,
Modelling, Multi-Objective Optimisation",
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DOI = "doi:10.1109/MetroAutomotive57488.2023.10219104",
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abstract = "a novel approach based on a Genetic Programming (GP)
algorithm is proposed to develop behavioural models for
Lithium batteries. In particular, this approach is
herein adopted to analytically correlate the battery
terminal voltage to its State of Charge (SoC) and
Charge rate (C-rate) for discharging current profiles.
The GP discovers the best possible analytical models,
from which the optimal one is selected by weighing
several criteria and enforcing a trade-off between the
accuracy and the simplicity of the obtained
mathematical function. The proposed models can be
considered an extension of the behavioural models that
are already in use, such as those based on equivalent
electrical circuits. This GP approach can overcome some
current limitations, such as the high time required to
perform experimental tests to estimate the parameters
of an equivalent electrical model (particularly
effective since it must be repeated with the battery
aging) and the need for some a-priory knowledge for the
model estimation. In this paper, a Lithium Titanate
Oxide battery has been considered as a case study,
analysing its behaviour for SoC comprised between
5percent and 95percent and C-rate between 0.25C and
4.0C. a preliminary study on GP-based modelling, in
which the best behavioural model is identified and
tested, with performances that encourage further
investigation of this kind of evolutionary approaches
by testing them with experimental characterisation
data.",
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notes = "Also known as \cite{10219104}",
- }
Genetic Programming entries for
Filippo Milano
Giulia Di Capua
Nunzio Oliva
Francesco Porpora
Carmine Bourelly
L Ferrigno
M Laracca
Citations