Prognostic of lithium-ion batteries using a combination of physical modeling and hybrid multi-layer perceptron particle filter

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Publication Details

Journal
Energy Reports
Published
2024-11-28
DOI
https://doi.org/10.1016/j.egyr.2024.11.058
Citations
3
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.61

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article

Prognostic of lithium-ion batteries using a combination of physical modeling and hybrid multi-layer perceptron particle filter

Jean‐Marc Bourinet, Francesco Cancelliere, Matteo Broggi, Sylvain Girard
3 citations
Energy Reports
Advanced Battery Technologies Research
0.61
article

Prognostic of lithium-ion batteries using a combination of physical modeling and hybrid multi-layer perceptron particle filter

Jean‐Marc Bourinet, Francesco Cancelliere, Matteo Broggi, Sylvain Girard
article en
3 citations

Abstract

No abstract available for this paper.
Energy ReportsVol. 12
Leibniz University Hannover (DE), Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (FR), PHIMECA (France) (FR)
Horizon 2020 Framework Programme, H2020 Marie Skłodowska-Curie Actions
Affordable and clean energy
Openalex Percentile: Top 32%
Advanced Battery Technologies Research
0.61
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