This paper presents a novel user-centered smart charging algorithm that follows different energy management scenarios for plug-in electric vehicles (PEVs) based on the user’s demand. The proposed approach consists of two separate stages: the model predictive control (MPC) with linear optimization and the rule-based fast method. This algorithm aims to reduce the total cost of energy exchanged with the grid from the user’s point of view while consuming the maximum photovoltaic (PV) power generation. In this regard, various scenarios are predicted and provided to the user in the form of charging costs and the state of health (SOH) of the PEV battery. Then, based on the user’s choices, the desired charging scenario is applied and the probable online small errors due to the other uncertainties are compensated. The effectiveness and flexibility of the proposed algorithm are evaluated through various simulation results for three PEVs at the same time. In addition, to provide further verification, the real-time part of the algorithm is executed in OPAL-RT simulator (OP5700) including the power hardware-in-the-loop (HIL) method.
