- Tytuł:
- Useful energy prediction model of a Lithium-ion cell operating on various duty cycles
- Autorzy:
- Burzyński, Damian
- Powiązania:
- https://bibliotekanauki.pl/articles/2087015.pdf
- Data publikacji:
- 2022
- Wydawca:
- Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
- Tematy:
-
cycle life modelling
lithium-ion battery
machine learning
predictive models
useful energy prediction - Opis:
- 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.
- Źródło:
-
Eksploatacja i Niezawodność; 2022, 24, 2; 317--329
1507-2711 - Pojawia się w:
- Eksploatacja i Niezawodność
- Dostawca treści:
- Biblioteka Nauki
Artykuł