Modelling of Li-Ion battery state-of-health with Gaussian processes

• Autorzy: Dudek Adrian , Baranowski Jerzy

Identyfikatory

DOI

10.24425/aee.2023.146042

• Języki publikacji: EN

Abstrakty

EN

The problem of lithium-ion cells, which degrade in time on their own and while used, causes a significant decrease in total capacity and an increase in inner resistance. So, it is important to have a way to predict and simulate the remaining usability of batteries. The process and description of cell degradation are very complex and depend on various variables. Classical methods are based, on the one hand, on fitting a somewhat arbitrary parametric function to laboratory data and, on the other hand, on electrochemical modelling of the physics of degradation. Alternative solutions are machine learning ones or non-parametric ones like support-vector machines or the Gaussian process (GP), which we used in this case. Besides using the GP, our approach is based on current knowledge of how to use non-parametric approaches for modeling the electrochemical state of batteries. It also uses two different ways of dealing with GP problems, like maximum likelihood type II (ML-II) methods and the Monte Carlo Markov Chain (MCMC) sampling.

Słowa kluczowe

EN

lithium-ion batteries; state of health; Gaussian process; diagnostics

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2023
• Tom: Vol. 72, nr 3
• Strony: 643--659
• Opis fizyczny: Bibliogr. 32 poz., fig., tab.

Twórcy

autor

Dudek Adrian

• Department of Automatic Control and Robotics, AGH University of Science and Technology Kraków, Poland

• https://orcid.org/0000-0002-3040-954X

autor

Baranowski Jerzy

• Department of Automatic Control and Robotics, AGH University of Science and Technology Kraków, Poland

• https://orcid.org/0000-0003-3313-581X

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).