Deep Reinforcement Learning Based Resource Allocation for Electric Vehicle Charging Stations with Priority Service

Aslınur Çolak

The demand for public fast charging stations is increasing with the number of electric vehicles on roads. The charging queues and waiting times get longer, especially during the winter season and on holidays. Priority based service at charging stations can provide shorter delay times to vehicles willing to pay more and lower charging prices for vehicles accepting to wait more. Existing studies use classical feedback control and simulation-based control methods to maintain the ratio of high and low priority vehicles’ delay times at the station’s target level. Reinforcement learning has been used successfully for real time control in environments with uncertainties. This study proposes a deep Q-Learning based real time resource allocation model for priority service in fast charging stations (DRL-EXP). Results show that the deep learning approach enables DRL-EXP to provide a more stable and faster response than the existing models. DRL-EXP is also applicable to other priority based service systems that act under uncertainties and require real time control.

Short Bio

Aslınur Çolak received her M.Sc. degree in 2023 from the Department of Industrial Engineering at TOBB University of Economics and Technology, where she is currently pursuing her Ph.D. She has been working as a Research Assistant since 2020 and has held research positions at TOBB ETU, TED University, and currently at Middle East Technical University (METU). Her research area focused on deep learning based intelligent resource allocation and real-time control of electric vehicle charging systems operating under uncertainty. Her work addresses operational challenges from congestion, demand variability and priority-based service mechanisms in fast charging stations. Her work integrates queueing theory, Markov decision processes, and simulation-based control to design adaptive resource allocation mechanisms that dynamically regulate delays among users. Her broader research interests include, simulation and learning-based control mechanisms and applications for complex systems.

Venue

Friday, March 6, 2026, 4.00 pm

IE Building, Halim Doğrusöz Auditorium (IE 03)