An Analytical Model for Lithium-Ion Batteries Based on Genetic Programming Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.7662

@InProceedings{Milano:2023:MetroAutomotive,

• author = "F. Milano and G. {Di Capua} and N. Oliva and F. Porpora and C. Bourelly and L. Ferrigno and M. Laracca",
• booktitle = "2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive)",
• title = "An Analytical Model for Lithium-Ion Batteries Based on Genetic Programming Approach",
• year = "2023",
• pages = "35--40",
• abstract = "In this paper, 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. This paper represents 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.",
• keywords = "genetic algorithms, genetic programming, Analytical models, Voltage, Mathematical models, Batteries, Behavioural sciences, Titanium compounds, Batteries, Modelling, Multi-Objective Optimisation",
• DOI = "doi:10.1109/MetroAutomotive57488.2023.10219104",
• month = jun,
• 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