A Review of Degradation Models and Remaining Useful Life Prediction for Testing Design and Predictive Maintenance of Lithium-Ion Batteries.
Sensors (Basel)
; 24(11)2024 May 24.
Article
em En
| MEDLINE
| ID: mdl-38894170
ABSTRACT
We present a novel decision-making framework for accelerated degradation tests and predictive maintenance that exploits prior knowledge and experimental data on the system's state. As a framework for sequential decision making in these areas, dynamic programming and reinforcement learning are considered, along with data-driven degradation learning when necessary. Furthermore, we illustrate both stochastic and machine learning degradation models, which are integrated in the framework, using data-driven methods. These methods are presented as a valuable tool for designing life-testing experiments and for maintaining lithium-ion batteries.
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Base de dados:
MEDLINE
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Itália