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

Author:

Patrizi Gabriele1ORCID,Martiri Luca2,Pievatolo Antonio3ORCID,Magrini Alessandro4ORCID,Meccariello Giovanni5ORCID,Cristaldi Loredana2ORCID,Nikiforova Nedka Dechkova4ORCID

Affiliation:

1. Department of Information Engineering, University of Florence, 50139 Florence, Italy

2. Department of Electronics, Information and Bioengineering, Polytechnic of Milan, 20133 Milan, Italy

3. Institute for Applied Mathematics and Information Technologies “E. Magenes”, National Research Council, 20133 Milan, Italy

4. Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, 50134 Florence, Italy

5. Institute of Sciences and Technologies for Energy and Sustainable Mobility, National Research Council, 80125 Naples, Italy

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.

Publisher

MDPI AG

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