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

verfasst von: Francesco Cancelliere, Sylvain Girard, Jean Marc Bourinet, Matteo Broggi
Abstract: With the European Union legislative push to phase out internal combustion engines by 2035, the demand for electric vehicles and efficient energy storage solutions, particularly lithium-ion batteries, is set to rise. Addressing this demand necessitates both the optimization of battery lifespan and the development of robust methodologies for real-time assessment of state of health and prediction of remaining useful life. This study introduces a novel hybrid grey-box prognostic and health management framework that combines a physical battery model with a multi-layer perceptron particle filter (MLP-PF) for real-time estimation of degradation parameters. The approach leverages an electrochemical model developed in Modelica to simulate battery voltage and track degradation parameters, thereby capturing the battery dynamic behavior over time. By integrating a data-driven MLP-PF, this method adapts the physical degradation parameters, ensuring ongoing and precise estimation of remaining useful life. Experimental validation, in terms of accuracy and confidence interval coverage, confirms the framework capability in prediction and relative quantification of uncertainties. These results underscore the framework practical utility for battery management systems in electric vehicles, providing an adaptable and accurate tool for decision-makers in battery maintenance and replacement.
Organisationseinheit(en): Institut für Risiko und Zuverlässigkeit
SFB 871: Regeneration komplexer Investitionsgüter
Externe Organisation(en): Phimeca Engineering S.A.
Université Clermont Auvergne (UCA)
Typ: Artikel
Journal: Energy Reports
Band: 12
Seiten: 5863-5874
Anzahl der Seiten: 12
Publikationsdatum: 12.2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Allgemeine Energie
Ziele für nachhaltige Entwicklung: SDG 7 – Erschwingliche und saubere Energie
Elektronische Version(en): https://doi.org/10.1016/j.egyr.2024.11.058 (Zugang: Offen)

BibTeX

@article{9617b8cd89c2402ea86bfce101008904,
title = "Prognostic of lithium-ion batteries using a combination of physical modeling and hybrid multi-layer perceptron particle filter",
abstract = "With the European Union legislative push to phase out internal combustion engines by 2035, the demand for electric vehicles and efficient energy storage solutions, particularly lithium-ion batteries, is set to rise. Addressing this demand necessitates both the optimization of battery lifespan and the development of robust methodologies for real-time assessment of state of health and prediction of remaining useful life. This study introduces a novel hybrid grey-box prognostic and health management framework that combines a physical battery model with a multi-layer perceptron particle filter (MLP-PF) for real-time estimation of degradation parameters. The approach leverages an electrochemical model developed in Modelica to simulate battery voltage and track degradation parameters, thereby capturing the battery dynamic behavior over time. By integrating a data-driven MLP-PF, this method adapts the physical degradation parameters, ensuring ongoing and precise estimation of remaining useful life. Experimental validation, in terms of accuracy and confidence interval coverage, confirms the framework capability in prediction and relative quantification of uncertainties. These results underscore the framework practical utility for battery management systems in electric vehicles, providing an adaptable and accurate tool for decision-makers in battery maintenance and replacement.",
keywords = "Artificial neural network, Grey-box, Lithium-ion batteries, Particle filter, Physical modeling",
author = "Francesco Cancelliere and Sylvain Girard and Bourinet, {Jean Marc} and Matteo Broggi",
note = "Publisher Copyright: {\textcopyright} 2024 The Authors",
year = "2024",
month = dec,
doi = "10.1016/j.egyr.2024.11.058",
language = "English",
volume = "12",
pages = "5863--5874",
journal = "Energy Reports",
issn = "2352-4847",
publisher = "Elsevier Ltd.",
}