Useful Energy Prediction Model of a Lithium-ion Cell Operating on Various Duty Cycles

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 (RUE_c) that can be transfered during a full cycle is variable, and also three different types of evolution of changes in RUE_c were identified. The paper presents a new non-parametric RUE_c prediction model based on Gaussian process regression. It was proven that the proposed methodology enables the RUE_c 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, for the first time, 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 RUE_c – the accumulated local effects model of the first and second order. Not only is the RUE_c prediction methodology presented in the paper characterised by high prediction accuracy when using small learning datasets, but it also shows high application potential in all kinds of battery management systems.