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Models for Battery Health Assessment: A Comparative Evaluation

Author

Listed:
  • Ester Vasta

    (Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy)

  • Tommaso Scimone

    (Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy)

  • Giovanni Nobile

    (Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy)

  • Otto Eberhardt

    (Enel Global Digital Solution, Viale Regina Margherita, 00198 Rome, Italy)

  • Daniele Dugo

    (Enel X, Contrada Passo Martino, 95121 Catania, Italy)

  • Massimiliano Maurizio De Benedetti

    (Enel X–Enel Foundation Fellow, Contrada Passo Martino, 95121 Catania, Italy)

  • Luigi Lanuzza

    (Enel X–Enel Foundation Fellow, Via Flaminia, 00189 Rome, Italy)

  • Giuseppe Scarcella

    (Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy)

  • Luca Patanè

    (Department of Engineering, University of Messina, Contrada di Dio, S. Agata, 98166 Messina, Italy)

  • Paolo Arena

    (Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy)

  • Mario Cacciato

    (Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy)

Abstract

Considering the importance of lithium-ion (Li-ion) batteries and the attention that the study of their degradation deserves, this work provides a review of the most important battery state of health (SOH) estimation methods. The different approaches proposed in the literature were analyzed, highlighting theoretical aspects, strengths, weaknesses and performance indices. In particular, three main categories were identified: experimental methods that include electrochemical impedance spectroscopy (EIS) and incremental capacity analysis (ICA), model-based methods that exploit equivalent electric circuit models (ECMs) and aging models (AMs) and, finally, data-driven approaches ranging from neural networks (NNs) to support vector regression (SVR). This work aims to depict a complete picture of the available techniques for SOH estimation, comparing the results obtained for different engineering applications.

Suggested Citation

  • Ester Vasta & Tommaso Scimone & Giovanni Nobile & Otto Eberhardt & Daniele Dugo & Massimiliano Maurizio De Benedetti & Luigi Lanuzza & Giuseppe Scarcella & Luca Patanè & Paolo Arena & Mario Cacciato, 2023. "Models for Battery Health Assessment: A Comparative Evaluation," Energies, MDPI, vol. 16(2), pages 1-34, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:632-:d:1025610
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    References listed on IDEAS

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