Ricerc@Sapienza

The steady-state dynamic response of a structure isolated by a nonlinear wire rope spring operating in the direction of gravity is experimentally studied. The isolated structure consists of two cantilever beams with a lumped mass at the tip. The force-displacement cycles provided by the isolator show a hysteretic behavior due to inter-wire friction and geometric nonlinearities. The restoring force is nonsymmetric exhibiting softening under compression and hardening under tension. The device rheological response is identified using experimental data and a suitable mechanical model.

A new measles epidemic model is proposed and identified by using real data relative to the number of confirmed infected patients in Italy in the period 1970-2018. The possibility of predicting the number of new infection is important for an efficient resource scheduling. Only in the last years great attention has been devoted to reliable data collection; therefore, in general, the model parameters identification is not an easy task. Moreover, the available data are 'corrupted' by human intervention, such as prevention campaign, or, whenever possible, vaccination.

In this paper, we propose an adaptive and convex combination of a recent class of nonlinear adaptive filters in different configurations. The proposed architecture relies on the properties of the adaptive combination of filters which exploits the capabilities of different constituents, thus adaptively providing at least the behavior of the best performing filter. The nonlinear functions involved in the adaptation process are based on spline function interpolation and their shapes can be modified during learning using gradient-based techniques.

The development of electrochemical cell models and of the related system identification procedures are of utmost importance for achieving effective management of electrochemical Energy Storage Systems. Specifically, accurate models are mandatory for performing effective estimation of the State of Charge (SoC) by means of Kalman Filtering approaches. Currently, some of the most promising models are those based on the equivalent circuit technique. However, these models are based on the standard definition of the SoC, which is related to the integral of the input current.

Accurate modeling of electrochemical cells is nowadays mandatory for achieving effective upgrades in the fields of energetic efficiency and sustainable mobility. Indeed, these models are often used for performing accurate State-of-Charge (SoC) estimations in energy storage systems used in microgrids or powering pure electric and hybrid cars. To this aim, a novel neural networks ensemble approach for modeling electrochemical cells is proposed in this paper.

Smart grids, microgrids, and pure electric powertrains are the key technologies for achieving the expected goals concerning the restraint of CO₂ emissions and global warming. In this context, an effective use of electrochemical energy storage systems (ESSs) is mandatory. In particular, accurate state of charge (SoC) estimations are helpful for improving the ESS performances. To this aim, developing accurate models of electrochemical cells is necessary for implementing effective SoC estimators. Therefore, a novel neural network modeling technique is proposed in this paper.