Li-Ion Batteries for Electric Vehicle Applications: An Overview of Accurate State of Charge/State of Health Estimation Methods

Adolfo Dannier (), Gianluca Brando, Mattia Ribera and Ivan Spina
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Adolfo Dannier: Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy
Gianluca Brando: Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy
Mattia Ribera: Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy
Ivan Spina: Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy

Energies, 2025, vol. 18, issue 4, 1-23

Abstract: Road transport significantly contributes to greenhouse gas emissions in all places where it is used and therefore also in Europe, prompting the EU to set ambitious objectives for CO 2 reduction. In order to reach these objectives, the automotive industry is transitioning to electric vehicles, utilizing electric powertrains powered by battery packs. However, the longevity and reliability of these batteries are critical concerns. This review paper focuses on the advanced diagnostic techniques for effective battery State of Charge (SoC) and State of Health (SoH) monitoring. Accurate SoC/SoH estimation is crucial for optimizing battery performance, avoiding premature degradation, and ensuring driver safety. By investigating these areas, this paper aims to contribute to the development of more sustainable and durable electric vehicles, supporting the transition to cleaner transportation systems.

Keywords: electric vehicle; battery; state of charge (SoC); state of health (SoH); monitoring; diagnostic (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
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