Correct management of the battery systems for electric vehicles, at pack, module and cell level, has critical importance to ensure safe and efficient operation. Proper control at cell level, requires estimation of battery statuses, such as the State-of-Charge and the State-of-Health, as they cannot directly measured during vehicle operation. With the increasing advances in sensor availability, edge computing and the development of big data, data-driven approaches have gained increasing interest. In particular, due to their great flexibility and potential in mapping non-linear relations within data, application of machine learning (ML) and neural networks (NNs) in SoH estimation have been thoroughly investigated. The numerous studies available in literature leveraged extracted different features from data to train NNs, or directly used measurements signal as input. Additionally, many studies available in literature are based on a limited number of publicly available datasets, which