In the next few years electric transport will become increasingly widespread. In fact it will be of paramount importance to reduce the polluting emissions produced by traditional engines. So, while waiting for electric trucks to take hold, it is important to start analysing the battery. Indeed, it is a key component even in non-electric vehicles: it provides the jolt of electricity necessary to power all the electrical components which are becoming more and more numerous. This thesis focuses on the application of a data-driven methodology to improve Battery Management and predict State Of Health (SOH) in heavy commercial vehicle’s. The analysis will exploit telematic data of trucks produced by IVECO: a vehicle tracking device is installed on board to send, receive and store telemetry data.