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

Author

Listed:
  • Giovane Ronei Sylvestrin

    (Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration—UNILA, Paraná City 85867-000, PR, Brazil
    Research Group on Energy & Energy Sustainability (GPEnSE), Academic Unit of Cabo de Santo Agostinho (UACSA), Federal Rural University of Pernambuco (UFRPE), Cabo de Santo Agostinho 54518-430, PE, Brazil)

  • Joylan Nunes Maciel

    (Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration—UNILA, Paraná City 85867-000, PR, Brazil
    Research Group on Energy & Energy Sustainability (GPEnSE), Academic Unit of Cabo de Santo Agostinho (UACSA), Federal Rural University of Pernambuco (UFRPE), Cabo de Santo Agostinho 54518-430, PE, 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
    Research Group on Energy & Energy Sustainability (GPEnSE), Academic Unit of Cabo de Santo Agostinho (UACSA), Federal Rural University of Pernambuco (UFRPE), Cabo de Santo Agostinho 54518-430, PE, Brazil
    Smart Grid Laboratory (LabREI), Center for Alternative and Renewable Research (CEAR), Federal University of Paraiba (UFPB), João Pessoa 58051-900, PB, Brazil)

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.

Suggested Citation

  • Giovane Ronei Sylvestrin & Joylan Nunes Maciel & Marcio Luís Munhoz Amorim & João Paulo Carmo & José A. Afonso & Sérgio F. Lopes & Oswaldo Hideo Ando Junior, 2025. "State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review," Energies, MDPI, vol. 18(3), pages 1-77, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:746-:d:1584934
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