A Novel Data-driven Incentive-based Charging Service Truncation Scheme To Improve the QoS Performance of Public EV Charging Stations

• Published in: 2024 IEEE International Conference on Smart Mobility (SM) pp. 147 - 152
• Main Authors: Al-Dahabreh, Nassr, Khabbaz, Maurice, Sayed, Mohammad Ali, Atallah, Ribal, Assi, Chadi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16-09-2024
• Subjects: Batteries; Benchmark testing; Charging; Data; Deployment; Electric vehicle charging; Incentive; Public; QoS; Quality of service; Resizing; Site; State of charge; Station; Strategies; Sustainable development; Urban areas
• DOI: 10.1109/SM63044.2024.10733434

This paper addresses the critically inadequate public charging infrastructure expansion strategies currently adopted by operators with a particular focus on the Quality-of-Service (QoS) perceived by EV users. A real-world case study of an urban Public EV Charging Station (P-EVCS) reveals the continuous deterioration of this P-EVCS's QoS performance despite the increased number of new P-EVCS deployments across the city. This turns out to be due to the upsurge in EV arrivals that the targeted P-EVCS is unable to cope with; a tangible proof of the ill-designed expansion scheme. To work around this, a Data-driven Incentive-based Charging Truncation (DICT) scheme is proposed herein. DICT encourages EV users to stop charging their EVs once batteries reach a State of Charge (SoC) of 80%. This is how DICT contributes to reducing the charging outlets' occupancy, decreases waiting times, and lowers the EV blocking probability. This scheme is benchmarked against other strategies, including site resizing and new in-proximity site deployments. A comprehensive data-driven simulation framework is developed to evaluate these schemes' performances and offer strategic insights and recommendations for public charging infrastructure enhancement stable QoS sustainability.