Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 5863-5874 |
| Seitenumfang | 12 |
| Fachzeitschrift | Energy Reports |
| Jahrgang | 12 |
| Frühes Online-Datum | 28 Nov. 2024 |
| Publikationsstatus | Veröffentlicht - Dez. 2024 |
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.
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in: Energy Reports, Jahrgang 12, 12.2024, S. 5863-5874.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Prognostic of lithium-ion batteries using a combination of physical modeling and hybrid multi-layer perceptron particle filter
AU - Cancelliere, Francesco
AU - Girard, Sylvain
AU - Bourinet, Jean Marc
AU - Broggi, Matteo
N1 - Publisher Copyright: © 2024 The Authors
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - Grey-box
KW - Lithium-ion batteries
KW - Particle filter
KW - Physical modeling
UR - http://www.scopus.com/inward/record.url?scp=85210128601&partnerID=8YFLogxK
U2 - 10.1016/j.egyr.2024.11.058
DO - 10.1016/j.egyr.2024.11.058
M3 - Article
AN - SCOPUS:85210128601
VL - 12
SP - 5863
EP - 5874
JO - Energy Reports
JF - Energy Reports
ER -