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Review
. 2024 May 24;24(11):3382.
doi: 10.3390/s24113382.

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

Gabriele Patrizi  1 , Luca Martiri  2 , Antonio Pievatolo  3 , Alessandro Magrini  4 , Giovanni Meccariello  5 , Loredana Cristaldi  2 , Nedka Dechkova Nikiforova  4
Affiliations
Review

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

Gabriele Patrizi et al. Sensors (Basel). .

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.

Keywords: accelerated degradation tests; degradation modelling; predictive maintenance; reinforcement learning; remaining useful life.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
SoH of the batteries tested in the NASA dataset [1]. Each SoH has been fitted with a single exponential model. The R2 value of each fitting is also reported.
Figure 2
Figure 2
SoH of all batteries in the Toyota dataset [2] divided into three batches. Each subplot shows the R2 value for an exponential model.
Figure 3
Figure 3
Discharge capacity and internal resistance of five batteries from the Toyota dataset.
See this image and copyright information in PMC

References

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