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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\u2019s 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.<\/jats:p>","DOI":"10.3390\/en18030746","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T08:57:46Z","timestamp":1738832266000},"page":"746","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["State of the Art in Electric Batteries\u2019 State-of-Health (SoH) Estimation with Machine Learning: A Review"],"prefix":"10.3390","volume":"18","author":[{"given":"Giovane Ronei","family":"Sylvestrin","sequence":"first","affiliation":[{"name":"Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration\u2014UNILA, Paran\u00e1 City 85867-000, PR, Brazil"},{"name":"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0725-6917","authenticated-orcid":false,"given":"Joylan Nunes","family":"Maciel","sequence":"additional","affiliation":[{"name":"Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration\u2014UNILA, Paran\u00e1 City 85867-000, PR, Brazil"},{"name":"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6548-7898","authenticated-orcid":false,"given":"Marcio Lu\u00eds Munhoz","family":"Amorim","sequence":"additional","affiliation":[{"name":"Group of Metamaterials Microwaves and Optics (GMeta), Department of Electrical Engineering (SEL), University of S\u00e3o Paulo (USP), Avenida Trabalhador S\u00e3o-Carlense, Nr. 400, Parque Industrial Arnold Schmidt, S\u00e3o Carlos 13566-590, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7955-7503","authenticated-orcid":false,"given":"Jo\u00e3o Paulo","family":"Carmo","sequence":"additional","affiliation":[{"name":"Group of Metamaterials Microwaves and Optics (GMeta), Department of Electrical Engineering (SEL), University of S\u00e3o Paulo (USP), Avenida Trabalhador S\u00e3o-Carlense, Nr. 400, Parque Industrial Arnold Schmidt, S\u00e3o Carlos 13566-590, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6275-9467","authenticated-orcid":false,"given":"Jos\u00e9 A.","family":"Afonso","sequence":"additional","affiliation":[{"name":"Center for Microelectromechanical Systems (CMEMS), University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8960-8498","authenticated-orcid":false,"given":"S\u00e9rgio F.","family":"Lopes","sequence":"additional","affiliation":[{"name":"Centro Algoritmi\/LASI, University of Minho, 4704-553 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6951-0063","authenticated-orcid":false,"given":"Oswaldo Hideo","family":"Ando Junior","sequence":"additional","affiliation":[{"name":"Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration\u2014UNILA, Paran\u00e1 City 85867-000, PR, Brazil"},{"name":"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"},{"name":"Smart Grid Laboratory (LabREI), Center for Alternative and Renewable Research (CEAR), Federal University of Paraiba (UFPB), Jo\u00e3o Pessoa 58051-900, PB, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1038\/s41560-019-0356-8","article-title":"Data-Driven Prediction of Battery Cycle Life before Capacity Degradation","volume":"4","author":"Severson","year":"2019","journal-title":"Nat. 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