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Useful energy prediction model of a Lithium-ion cell operating on various duty cycles
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Poznan University of Technology, Faculty of Control, Robotics and Electrical Engineering, ul. Piotrowo 3a, 60-965 Poznan, Poland
Publication date: 2022-06-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2022;24(2):317-329
  • A new non-parametric useful energy model for long-term prediction was developed.
  • Developed model takes into account the lifetime degradation of the cell.
  • Identification of three types of RUEc evolution over exploitation period of the cells.
  • XAI techniques were used to quantify effect of model parameters on RUEc.
  • The proposed methodology can be applied to electrochemical cells of other types.
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.
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