A Behavioral Model for Lithium Batteries based on Genetic Programming

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@InProceedings{Di-Capua:2023:ISCAS,

• author = "G. {Di Capua} and N. Oliva and F. Milano and C. Bourelly and F. Porpora and A. Maffucci and N. Femia",
• booktitle = "2023 IEEE International Symposium on Circuits and Systems (ISCAS)",
• title = "A Behavioral Model for Lithium Batteries based on Genetic Programming",
• year = "2023",
• abstract = "This paper proposes a novel approach to derive analytical behavioural models of Lithium batteries, based on a Genetic Programming Algorithm (GPA). This approach is used to analytically relate the battery voltage to its State-of-Charge (SoC) and Charge/discharge rate (C-rate), during a battery discharge phase. The GPA generates optimal candidate analytical models, where the preferred one is selected by evaluating suitable metrics and imposing a sound trade-off between simplicity and accuracy. The GPA proposed model can be seen as a generalisation of the equivalent circuit models currently used for batteries, with the possible advantage to overcome some inherent limits, like the extensive laboratory characterisation for model parameters evaluation. The presented case-study refers to a Lithium Titanate Oxide battery, with SoC values going from 5 to 95percent, at C-rate values between 0.25C and 4.0C.",
• keywords = "genetic algorithms, genetic programming, Measurement, Analytical models, Voltage, Lithium batteries, Behavioural sciences, Batteries, Modelling, Multi-Objective Optimisation",
• DOI = "doi:10.1109/ISCAS46773.2023.10181456",
• ISSN = "2158-1525",
• month = may,
• notes = "Also known as \cite{10181456}",

}

Genetic Programming entries for Giulia Di Capua Nunzio Oliva Filippo Milano Carmine Bourelly Francesco Porpora Antonio Maffucci Nicola Femia

Citations