Ricerc@Sapienza

This paper presents a decentralized Model Predictive Control (MPC) for Plug-in Electric Vehicles (PEVs) charging, in presence of both network and drivers' requirements. The open loop optimal control problem at the basis of MPC is modeled as a consesus with regularization optimization problem and solved by means of the decentralized Alternating Direction Method of Multipliers (ADMM).

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.

Nowadays, the equivalent circuit approach is one of the most used methods for modeling electrochemical cells. The main advantage consists in the beneficial trade-off between accuracy and complexity that makes these models very suitable for the State of Charge (SoC) estimation task. However, parameters identification could be difficult to perform, requiring very long and specific tests upon the cell. Thus, a more flexible identification procedure based on an improved Particle Swarm Optimization that does not require specific and time consuming measurements is proposed and validated.