A Charge Curve and Battery Management System Aware Optimal Charging Scheduling Framework for Electric Vehicle Fast Charging Stations With Heterogeneous Customer Mix

Fast charging has the potential to address range anxiety and facilitate the adoption of electric vehicles (EVs). However, the distinct characteristics of public fast charging, such as short dwell times, diverse EV charge curves, and uncertain battery management systems (BMS), makes accurate coordination challenging while using traditional EV charging scheduling algorithms. This paper proposes a charge curve and BMS aware EV charging scheduling framework for capacity-constrained fast charging stations (FCSs) with a heterogeneous customer mix. The proposed framework involves a lexicographic mixed-integer optimization problem that is solved using a receding horizon scheme. The main contributions are: 1) an EV charge curve model that accurately reflects fast charging characteristics and can be efficiently incorporated into an optimization problem; 2) the introduction of two customer types to ensure high and fair quality of service (QoS) among customers with diverse preferences and EV specifications; 3) a four-criteria lexicographic optimization formulation that yields a pareto-optimal solution and provides additional benefits such as increased intermediate state-of-charge levels and reduced charge ramps; and 4) the adoption of a receding horizon scheme and a bounding heuristic that effectively addresses uncertainties associated with EV arrivals and BMS actions. Extensive case studies reveal that the proposed framework consistently outperforms the three benchmark methods by achieving a charge fairness improvement of up to 10.41% compared to the second best performer, while maintaining over 90% fairness score even under various BMS uncertainties and remaining computationally efficient for practical use in fast charging stations.