LAPSE:2023.6176
Published Article
LAPSE:2023.6176
Models for Battery Health Assessment: A Comparative Evaluation
February 23, 2023
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.
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.
Record ID
Keywords
aging model, electrochemical impedance spectroscopy, equivalent electric circuit model, incremental capacity analysis, neural network, state of health, support vector regression
Suggested Citation
Vasta E, Scimone T, Nobile G, Eberhardt O, Dugo D, De Benedetti MM, Lanuzza L, Scarcella G, Patanè L, Arena P, Cacciato M. Models for Battery Health Assessment: A Comparative Evaluation. (2023). LAPSE:2023.6176
Author Affiliations
Vasta E: Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy [ORCID]
Scimone T: Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy [ORCID]
Nobile G: Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy
Eberhardt O: Enel Global Digital Solution, Viale Regina Margherita, 00198 Rome, Italy
Dugo D: Enel X, Contrada Passo Martino, 95121 Catania, Italy
De Benedetti MM: Enel X−Enel Foundation Fellow, Contrada Passo Martino, 95121 Catania, Italy [ORCID]
Lanuzza L: Enel X−Enel Foundation Fellow, Via Flaminia, 00189 Rome, Italy
Scarcella G: Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy [ORCID]
Patanè L: Department of Engineering, University of Messina, Contrada di Dio, S. Agata, 98166 Messina, Italy
Arena P: Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy
Cacciato M: Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy [ORCID]
Scimone T: Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy [ORCID]
Nobile G: Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy
Eberhardt O: Enel Global Digital Solution, Viale Regina Margherita, 00198 Rome, Italy
Dugo D: Enel X, Contrada Passo Martino, 95121 Catania, Italy
De Benedetti MM: Enel X−Enel Foundation Fellow, Contrada Passo Martino, 95121 Catania, Italy [ORCID]
Lanuzza L: Enel X−Enel Foundation Fellow, Via Flaminia, 00189 Rome, Italy
Scarcella G: Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy [ORCID]
Patanè L: Department of Engineering, University of Messina, Contrada di Dio, S. Agata, 98166 Messina, Italy
Arena P: Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy
Cacciato M: Department of Electrical, Electronics Engineering and Computer Science (DIEEI), University of Catania, Viale Andrea Doria, 95125 Catania, Italy [ORCID]
Journal Name
Energies
Volume
16
Issue
2
First Page
632
Year
2023
Publication Date
2023-01-05
ISSN
1996-1073
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Original Submission
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PII: en16020632, Publication Type: Review
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LAPSE:2023.6176
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https://doi.org/10.3390/en16020632
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