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International Conference on the Applications of Evolutionary Computation (Part of EvoStar)

EvoApplications 2023: Applications of Evolutionary Computation pp 461–474Cite as

Using Genetic Programming to Learn Behavioral Models of Lithium Batteries

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13989))

Abstract

Li-ion batteries play a key role in the sustainable development scenario, since they can allow a better management of renewable energy resources. The performances of Li-ion batteries are influenced by several factors. For this reason, accurate and reliable models of these batteries are needed, not only in the design phase, but also in real operating conditions. In this paper, we present a novel approach based on Genetic Programming (GP) for the voltage prediction of a Lithium Titanate Oxide battery. The proposed approach uses a multi-objective optimization strategy. The evolved models take in input the State-of-Charge (SoC) and provide as output the Charge/discharge rate (C-rate), which is used to evaluate the impact of the charge or discharge speed on the voltage. The experimental results showed that our approach is able to generate optimal candidate analytical models, where the choice of the preferred one is made by evaluating suitable metrics and imposing a sound trade-off between simplicity and accuracy. These results also proved that our GP-based behavioral modeling is more reliable and flexible than those based on a standard machine learning approach, like a neural network.

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Acknowledgments

This work was supported by MOST, the Italian National Center for Sustainable Mobility, funded by the Italian Ministry of University and Research (MUR) in 2022–2025, by the “Innovazione per il Controllo avanzato e la gestione di Grid Energetiche” project within the Puglia FESR 2014 – 2020 (CUP B81B22000020007), and by Power4Future S.p.A. under the frame of the grant CDS000944, funded by the Italian Ministry of Enterprises and Made in Italy (MISE).

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Authors and Affiliations

  1. DIEI, University of Cassino and Southern Lazio, Cassino, FR, Italy

    G. Di Capua, C. Bourelly, C. De Stefano, F. Fontanella, F. Milano, M. Molinara & F. Porpora

  2. DIEM, University of Salerno, Fisciano, SA, Italy

    N. Oliva

  3. E-Lectra s.r.l., Cassino, FR, Italy

    F. Porpora

Authors
  1. G. Di Capua
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  2. C. Bourelly
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  3. C. De Stefano
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  4. F. Fontanella
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  5. F. Milano
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  6. M. Molinara
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  7. N. Oliva
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  8. F. Porpora
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Corresponding author

Correspondence to F. Fontanella .

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Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    João Correia

  2. University of York, York, UK

    Stephen Smith

  3. Al Hussein Technical University, Amman, Jordan

    Raneem Qaddoura

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Di Capua, G. et al. (2023). Using Genetic Programming to Learn Behavioral Models of Lithium Batteries. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_30

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  • DOI: https://doi.org/10.1007/978-3-031-30229-9_30

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  • Online ISBN: 978-3-031-30229-9

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