State of health (SOH) is a key parameter of lithium-ion battery. As the battery ages an accurate SOH estimation plays a important role in battery management system (BMS). This thesis refers to a dataset from a laboratory test campaign conducted at LIM to train a LSTM network for estimating the SOH variation. The dataset is composed by 55 charging-discharging cycles and each of them has 43 different usage profiles. In order to age the battery, the entire test cycle lasted for several months. SOH decayed from initial 100% to 82%. In order to test the robustness of the network it uses three different profiles: charging profile, pulse profile and polarized random walk profile.