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

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 continu...

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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
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Abstract 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.
AbstractList 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.
Author Sayed, Mohammad Ali
Assi, Chadi
Al-Dahabreh, Nassr
Khabbaz, Maurice
Atallah, Ribal
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  organization: Concordia University,Montréal,Canada,H3G 1M8
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Snippet This paper addresses the critically inadequate public charging infrastructure expansion strategies currently adopted by operators with a particular focus on...
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StartPage 147
SubjectTerms 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
Title A Novel Data-driven Incentive-based Charging Service Truncation Scheme To Improve the QoS Performance of Public EV Charging Stations
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