State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review

Giovane Ronei Sylvestrin, Joylan Nunes Maciel, Marcio Luís Munhoz Amorim, João Paulo Carmo, José A. Afonso (), Sérgio F. Lopes and Oswaldo Hideo Ando Junior ()
Additional contact information
Giovane Ronei Sylvestrin: Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration—UNILA, Paraná City 85867-000, PR, Brazil
Joylan Nunes Maciel: Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration—UNILA, Paraná City 85867-000, PR, Brazil
Marcio Luís Munhoz Amorim: Group of Metamaterials Microwaves and Optics (GMeta), Department of Electrical Engineering (SEL), University of São Paulo (USP), Avenida Trabalhador São-Carlense, Nr. 400, Parque Industrial Arnold Schmidt, São Carlos 13566-590, SP, Brazil
João Paulo Carmo: Group of Metamaterials Microwaves and Optics (GMeta), Department of Electrical Engineering (SEL), University of São Paulo (USP), Avenida Trabalhador São-Carlense, Nr. 400, Parque Industrial Arnold Schmidt, São Carlos 13566-590, SP, Brazil
José A. Afonso: Center for Microelectromechanical Systems (CMEMS), University of Minho, 4800-058 Guimarães, Portugal
Sérgio F. Lopes: Centro Algoritmi/LASI, University of Minho, 4704-553 Guimarães, Portugal
Oswaldo Hideo Ando Junior: Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration—UNILA, Paraná City 85867-000, PR, Brazil

Energies, 2025, vol. 18, issue 3, 1-77

Abstract: The sustainable reuse of batteries after their first life in electric vehicles requires accurate state-of-health (SoH) estimation to ensure safe and efficient repurposing. This study applies the systematic ProKnow-C methodology to analyze the state of the art in SoH estimation using machine learning (ML). A bibliographic portfolio of 534 papers (from 2018 onward) was constructed, revealing key research trends. Public datasets are increasingly favored, appearing in 60% of the studies and reaching 76% in 2023. Among 12 identified sources covering 20 datasets from different lithium battery technologies, NASA’s Prognostics Center of Excellence contributes 51% of them. Deep learning (DL) dominates the field, comprising 57.5% of the implementations, with LSTM networks used in 22% of the cases. This study also explores hybrid models and the emerging role of transfer learning (TL) in improving SoH prediction accuracy. This study also highlights the potential applications of SoH predictions in energy informatics and smart systems, such as smart grids and Internet-of-Things (IoT) devices. By integrating accurate SoH estimates into real-time monitoring systems and wireless sensor networks, it is possible to enhance energy efficiency, optimize battery management, and promote sustainable energy practices. These applications reinforce the relevance of machine-learning-based SoH predictions in improving the resilience and sustainability of energy systems. Finally, an assessment of implemented algorithms and their performances provides a structured overview of the field, identifying opportunities for future advancements.

Keywords: state of health; battery; machine learning; ProKnow-C; public datasets; energy informatics; smart grids; internet of things; deep learning (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: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/3/746/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/3/746/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:3:p:746-:d:1584934

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().