Models for Battery Health Assessment: A Comparative Evaluation

Ester Vasta, Tommaso Scimone, Giovanni Nobile, Otto Eberhardt, Daniele Dugo, Massimiliano Maurizio De Benedetti, Luigi Lanuzza, Giuseppe Scarcella, Luca Patanè, Paolo Arena () and Mario Cacciato
Additional contact information
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

Energies, 2023, vol. 16, issue 2, 1-34

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.

Keywords: state of health; incremental capacity analysis; electrochemical impedance spectroscopy; equivalent electric circuit model; aging model; neural network; support vector regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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