Because of its high power and energy density, the use of Li-ion batteries as a major energy storage system has increased especially in the automotive area. Apart from service disruption, the failure of one of such batteries can cause high costs of repair, and even life threatening risks, in case of overheating or short circuiting. In order to pre-vent such severe failures from occurring, the monitoring of Lithium-ion batteries state of health must be studied and purposely designed, case by case for each new application. This Master research work addresses the problem of computing an State of Health for a Kinetic Energy Recovery System application, where Li-ion batteries with high power density are needed. Finally, an Extended Kalman Filter is implemented in order to follow the internal resistance degradation, the main aging property for this application.