Building energy management and Electric Vehicle charging considering battery degradation and random vehicles' arrivals and departures *

We study the energy management and Electric Vehicle (EV) charging optimization problem for a smart building integrating Renewable Energy Source (RES) considering battery degradation cost and random EVs' arrivals/departures. Our design employs a general non-linear degradation cost model for lithium battery in which the degradation cost is a function of the Depth of Discharge (DoD). We develop a two-layer model predictive control (MPC) based framework to effectively deal with the uncertainties in EVs' arrivals/departures and erroneous prediction of electricity load, RES energy, and energy price. Specifically, the upper layer with a longer optimization horizon aims to plan the energy for the EVs and ESS so that the total energy usage and battery degradation cost is minimized while satisfying the required energy levels of EVs before their departures. The lower layer optimizes the charging/discharging operations of the EVs and ESS over a shorter horizon following the energy planned by the upper layer. In extensive numerical studies, we study the impacts of different parameters on the achievable costs. Moreover, the proposed design is compared with different baselines using different cost models in the two layers and the one-layer MPC-based framework to demonstrate the efficacy of our proposed framework.