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Ensemble Surrogate Models for Fast LIB Performance Predictions

Author

Listed:
  • Marco Quartulli

    (Vicomtech, Basque Research and Technology Alliance, Mikeletegi 57, 20009 Donostia-San Sebastián (ES), Spain)

  • Amaia Gil

    (Vicomtech, Basque Research and Technology Alliance, Mikeletegi 57, 20009 Donostia-San Sebastián (ES), Spain)

  • Ane Miren Florez-Tapia

    (Vicomtech, Basque Research and Technology Alliance, Mikeletegi 57, 20009 Donostia-San Sebastián (ES), Spain)

  • Pablo Cereijo

    (CIDETEC, Basque Research and Technology Alliance, Pº Miramón 196, 20014 Donostia-San Sebastián (ES), Spain)

  • Elixabete Ayerbe

    (CIDETEC, Basque Research and Technology Alliance, Pº Miramón 196, 20014 Donostia-San Sebastián (ES), Spain)

  • Igor G. Olaizola

    (Vicomtech, Basque Research and Technology Alliance, Mikeletegi 57, 20009 Donostia-San Sebastián (ES), Spain)

Abstract

Battery Cell design and control have been widely explored through modeling and simulation. On the one hand, Doyle’s pseudo-two-dimensional (P2D) model and Single Particle Models are among the most popular electrochemical models capable of predicting battery performance and therefore guiding cell characterization. On the other hand, empirical models obtained, for example, by Machine Learning (ML) methods represent a simpler and computationally more efficient complement to electrochemical models and have been widely used for Battery Management System (BMS) control purposes. This article proposes ML-based ensemble models to be used for the estimation of the performance of an LIB cell across a wide range of input material characteristics and parameters and evaluates 1. Deep Learning ensembles for simulation convergence classification and 2. structured regressors for battery energy and power predictions. The results represent an improvement on state-of-the-art LIB surrogate models and indicate that deep ensembles represent a promising direction for battery modeling and design.

Suggested Citation

  • Marco Quartulli & Amaia Gil & Ane Miren Florez-Tapia & Pablo Cereijo & Elixabete Ayerbe & Igor G. Olaizola, 2021. "Ensemble Surrogate Models for Fast LIB Performance Predictions," Energies, MDPI, vol. 14(14), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4115-:d:590463
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    References listed on IDEAS

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    1. Tipaluck Krityakierne & Taimoor Akhtar & Christine A. Shoemaker, 2016. "SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems," Journal of Global Optimization, Springer, vol. 66(3), pages 417-437, November.
    2. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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    1. Lillo-Bravo, I. & Vera-Medina, J. & Fernandez-Peruchena, C. & Perez-Aparicio, E. & Lopez-Alvarez, J.A. & Delgado-Sanchez, J.M., 2023. "Random Forest model to predict solar water heating system performance," Renewable Energy, Elsevier, vol. 216(C).

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