Useful energy prediction model of a Lithium-ion cell operating on various duty cycles

Burzyński, Damian

doi:10.17531/ein.2022.2.13

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Tytuł artykułu

Useful energy prediction model of a Lithium-ion cell operating on various duty cycles

Autorzy

Burzyński Damian

Treść / Zawartość

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DOI

10.17531/ein.2022.2.13

Warianty tytułu

Języki publikacji

Abstrakty

The paper deals with the subject of the prediction of useful energy during the cycling of a lithium-ion cell (LIC), using machine learning-based techniques. It was demonstrated that depending on the combination of cycling parameters, the useful energy (RUEc) that can be transferred during a full cycle is variable, and also three different types of evolution of changes in RUEc were identified. The paper presents a new non-parametric RUEc prediction model based on Gaussian process regression. It was proven that the proposed methodology enables the RUEc prediction for LICs discharged, above the depth of discharge, at a level of 70% with an acceptable error, which is confirmed for new load profiles. Furthermore, techniques associated with explainable artificial intelligence were applied to determine the significance of model input parameters – the variable importance method – and to determine the quantitative effect of individual model parameters (their reciprocal interaction) on RUEc – the accumulated local effects model of the first and second order.

Słowa kluczowe

cycle life modelling lithium-ion battery machine learning predictive models useful energy prediction

Wydawca

Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne

Czasopismo

Eksploatacja i Niezawodność

Rocznik

2022

Tom

Vol. 24, no. 2

Strony

317--329

Opis fizyczny

Bibliogr. 69 poz., rys., tab.

Twórcy

autor

Burzyński Damian

damian.burzynski@put.poznan.pl

Poznan University of Technology, Faculty of Control, Robotics and Electrical Engineering, ul. Piotrowo 3a, 60-965 Poznan, Poland

https://orcid.org/0000-0001-7874-1376

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

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Bibliografia

Identyfikator YADDA

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