Nowadays, powertrain hybridization represents one of the most viable solutions to reduce the carbon footprint of the transport sector. Nevertheless, its potential can be fully exploited only through the design of suitable Energy Management Strategies capable of optimally coordinating the multiple power sources installed onboard. In this context, accurate speed forecasting, enabled by increasing levels of vehicle connectivity, can support the development of predictive control strategies that further enhance the benefits of hybridization through a more efficient optimization of energy flows. Therefore, this thesis work investigates the use of predictive artificial intelligence for energy management enhancement through vehicle speed forecasting. Specifically, the study employs a Long Short-Term Memory (LSTM) neural network trained on synthetic traffic data generated with the open-source software SUMO (Simulation of Urban Mobility), simulating a typical working day in the city of Turin.