Li-ion batteries have become the main technology for the electric mobility. One of the most urgent challenges concerns the development of reliable methods for their state-of-health (SOH) diagnosis and consequent estimation of remaining useful life. For the electric mobility, capacity degradation prediction is highly critical to ensure service availability (km range) and life duration. In most of the existing studies, batteries cells go through standard charge/discharge patterns in laboratory conditions to study the ageing processes. However, this approach does not reflect the real world conditions of electric vehicles (EV) batteries. The goal of this work is to develop a data-driven approach suitable for on-board SOH estimation of electric vehicles batteries, based on data acquired at different levels, from single cells to full battery packs.