Predictive Scheduling Framework for Electric Vehicles With Uncertainties of User Behaviors.

The randomness of user behaviors plays a significant role in electric vehicle (EV) scheduling problems, especially when the power supply for EV supply equipment (EVSE) is limited. Existing EV scheduling methods do not consider this limitation and assume charging session parameters, such as stay duration and energy demand values, are perfectly known, which is not realistic in practice. In this paper, based on real-world implementations of networked EVSEs on University of California at Los Angeles campus, we developed a predictive scheduling framework, including a predictive control paradigm and a kernel-based session parameter estimator. Specifically, the scheduling service periodically computes for cost-efficient solutions, considering the predicted session parameters, by the adaptive kernel-based estimator with improved estimation accuracies. We also consider the power sharing strategy of existing EVSEs and formulate the virtual load constraint to handle the future EV arrivals with unexpected energy demand. To validate the proposed framework, 20-fold cross validation is performed on the historical dataset of charging behaviors for over one-year period. The simulation results demonstrate that average unit energy cost per kWh can be reduced by 29.42% with the proposed scheduling framework and 66.71% by further integrating solar generations with the given capacity, after the initial infrastructure investment. The effectiveness of kernel-based estimator, virtual load constraint, and event-based control scheme are also discussed in detail.