A Review of Degradation Models and Remaining Useful Life Prediction for Testing Design and Predictive Maintenance of Lithium-Ion Batteries

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 c...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-05, Vol.24 (11), p.3382
Main Authors: Patrizi, Gabriele, Martiri, Luca, Pievatolo, Antonio, Magrini, Alessandro, Meccariello, Giovanni, Cristaldi, Loredana, Nikiforova, Nedka Dechkova
Format: Article
Language:English
Subjects:
accelerated degradation tests
Algorithms
Analysis
Artificial intelligence
Batteries
Chemistry
Datasets
Decision-making
degradation modelling
Design of experiments
Kalman filters
Lithium
Machine learning
Neural networks
predictive maintenance
reinforcement learning
remaining useful life
Review
Support vector machines
Trends
Useful life
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24113382