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 (<i>RUE<sub>c</sub></i>) that
can be transfered during a full cycle is variable, and also three different
types of evolution of changes in <i>RUE<sub>c</sub></i>
were identified. The paper presents a new non-parametric <i>RUE<sub>c</sub></i> prediction model based on Gaussian process
regression. It was proven that the proposed methodology enables the <i>RUE<sub>c</sub></i> 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 <i>RUE<sub>c</sub></i> – the accumulated local effects model of the first
and second order. Not only is the <i>RUE<sub>c</sub></i>
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.
Lithium Ion Cell Overcharge in the Absence of Battery Management Unit
Failure
