A cognitive stochastic approximation approach to optimal charging schedule in electric vehicle stations

This paper proposes a Cognitive Stochastic Approximation (CSA)-based optimization method for charging an EV (electric vehicle) fleet, using a single, aggregate battery model. The charging station can either utilize the batteries of the parked vehicles to charge the vehicles before they leave, or can use power from the grid. The objective is to optimize the charging task with minimum energy costs, possibly taking into account price variations in the electricity price. The main advantage of the proposed approach is that it provides a nearly to optimal solution in the presence of uncertain charging/discharging dynamics. The method is evaluated through a numerical model of a grid-connected charging station. Four scenarios with different electricity price models are studied. The CSA optimization results are compared with the results obtained by a rule-based charging algorithm and by an open-loop optimal control algorithm: the results illustrate the advantages of the proposed CSA algorithm in minimizing the charging cost, satisfying the aggregate battery charge sustaining conditions and providing robust solutions in the presence of time-varying vehicle schedules.