Numerous works have been undertaken to tackle each stage of battery development: from the automatic discovery of complex battery mechanisms , prediction of the rest of useful life (RUL) , evaluation of SoH , providing insights into battery production processes and their interdependencies , guiding battery design , optimizing cycling profiles , early prediction of the battery cell lifetime , approximating failure distributions or prediction of lifelong performance immediately after cell manufacturing However, in the context of battery recycling, the utilization of ML has remained relatively constrained. Only a small number of studies have delved into ML techniques specifically targeting various stages of the recycling process. This scarcity is often attributed to the inadequate availability of comprehensive battery data, especially for batteries that have undergone cycles until the EoL stage.