Methods for state of health estimation for lithium-ion batteries: An essential review
Electric vehicles (EVs) are a practical and suitable choice for reducing the pollution rate caused by combustible engines of conventional cars. The lithium-ion batteries (LIB) serve as a support for energy storage in EVs owing to their benefits and advantages. To ensure their optimal performance and...
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
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| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00071.pdf |
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| author | Rhdifa Houda Ammar Abderazzak Bouattane Omar |
| author_facet | Rhdifa Houda Ammar Abderazzak Bouattane Omar |
| author_sort | Rhdifa Houda |
| collection | DOAJ |
| description | Electric vehicles (EVs) are a practical and suitable choice for reducing the pollution rate caused by combustible engines of conventional cars. The lithium-ion batteries (LIB) serve as a support for energy storage in EVs owing to their benefits and advantages. To ensure their optimal performance and working under safe conditions the state of health SOH of battery has to be accurately estimated. In this paper, the main estimation techniques, namely, model-based, and data-driven approaches are explained with a brief look at their several stages. Thus, two examples are presented for each method: neural networks (NN) and support vector machines (SVM) for data-driven, the combination of variable forgetting factor recursive least squares (VFF-RLS) with adaptive unscented Kalman filter (AUKF) and particle swarm optimization (PSO), genetic algorithm (GA), particle filter (PF), recursive least squares (RLS) for model-based method to show how each method is applied. Finally, a list of advantages and drawbacks of some parameter identification and SOH estimation methods is prepared, and then some other related works are referred to. |
| format | Article |
| id | doaj-art-fdf17379e38c4513baf0fdfcb7075213 |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-fdf17379e38c4513baf0fdfcb70752132025-02-05T10:47:15ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016010007110.1051/e3sconf/202560100071e3sconf_icegc2024_00071Methods for state of health estimation for lithium-ion batteries: An essential reviewRhdifa Houda0Ammar Abderazzak1Bouattane Omar2EEIS, ENSET Mohammedia, University Hassan II CasablancaEEIS, ENSET Mohammedia, University Hassan II CasablancaEEIS, ENSET Mohammedia, University Hassan II CasablancaElectric vehicles (EVs) are a practical and suitable choice for reducing the pollution rate caused by combustible engines of conventional cars. The lithium-ion batteries (LIB) serve as a support for energy storage in EVs owing to their benefits and advantages. To ensure their optimal performance and working under safe conditions the state of health SOH of battery has to be accurately estimated. In this paper, the main estimation techniques, namely, model-based, and data-driven approaches are explained with a brief look at their several stages. Thus, two examples are presented for each method: neural networks (NN) and support vector machines (SVM) for data-driven, the combination of variable forgetting factor recursive least squares (VFF-RLS) with adaptive unscented Kalman filter (AUKF) and particle swarm optimization (PSO), genetic algorithm (GA), particle filter (PF), recursive least squares (RLS) for model-based method to show how each method is applied. Finally, a list of advantages and drawbacks of some parameter identification and SOH estimation methods is prepared, and then some other related works are referred to.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00071.pdflithium-ion batteries (lib)state of health (soh)neural networks (nn)support vector machines (svm)particle swarm optimization (pso)genetic algorithm (ga)recursive least square (rls)adaptive unscented kalman filter (aukf) |
| spellingShingle | Rhdifa Houda Ammar Abderazzak Bouattane Omar Methods for state of health estimation for lithium-ion batteries: An essential review E3S Web of Conferences lithium-ion batteries (lib) state of health (soh) neural networks (nn) support vector machines (svm) particle swarm optimization (pso) genetic algorithm (ga) recursive least square (rls) adaptive unscented kalman filter (aukf) |
| title | Methods for state of health estimation for lithium-ion batteries: An essential review |
| title_full | Methods for state of health estimation for lithium-ion batteries: An essential review |
| title_fullStr | Methods for state of health estimation for lithium-ion batteries: An essential review |
| title_full_unstemmed | Methods for state of health estimation for lithium-ion batteries: An essential review |
| title_short | Methods for state of health estimation for lithium-ion batteries: An essential review |
| title_sort | methods for state of health estimation for lithium ion batteries an essential review |
| topic | lithium-ion batteries (lib) state of health (soh) neural networks (nn) support vector machines (svm) particle swarm optimization (pso) genetic algorithm (ga) recursive least square (rls) adaptive unscented kalman filter (aukf) |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00071.pdf |
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