Abstract
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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