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


[ 1 ] Instytut Elektrotechniki i Elektroniki Przemysłowej, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.2] Automation, electronics and electrical engineering

Year of publication


Published in

Eksploatacja i Niezawodność – Maintenance and Reliability

Journal year: 2022 | Journal volume: vol. 24 | Journal number: no. 2

Article type

scientific article

Publication language


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

EN 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.

Date of online publication


Pages (from - to)

317 - 328




License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Release date


Date of Open Access to the publication

at the time of publication

Full text of article

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Ministry points / journal


Impact Factor

2.742 [List 2021]

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