A Data-Driven Framework for Improving Public EV Charging Infrastructure: Modeling and Forecasting

• URL: http://arxiv.org/abs/2312.05333v1
• Date: Fri, 8 Dec 2023 19:37:15 GMT
• Title: A Data-Driven Framework for Improving Public EV Charging Infrastructure: Modeling and Forecasting
• Authors: Nassr Al-Dahabreh, Mohammad Ali Sayed, Khaled Sarieddine, Mohamed Elhattab, Maurice Khabbaz, Ribal Atallah, Chadi Assi
• Abstract summary: It is suspected that the existing charging infrastructure will soon be no longer capable of sustaining the rapidly growing charging demands. Without suitable QoE metrics, operators, today, face remarkable difficulty in assessing the performance of EV Charging Stations. This paper aims at filling this gap through the formulation of novel and original critical QoE performance metrics.
• Score: 13.950084838642228
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work presents an investigation and assessment framework, which, supported by realistic data, aims at provisioning operators with in-depth insights into the consumer-perceived Quality-of-Experience (QoE) at public Electric Vehicle (EV) charging infrastructures. Motivated by the unprecedented EV market growth, it is suspected that the existing charging infrastructure will soon be no longer capable of sustaining the rapidly growing charging demands; let alone that the currently adopted ad hoc infrastructure expansion strategies seem to be far from contributing any quality service sustainability solutions that tangibly reduce (ultimately mitigate) the severity of this problem. Without suitable QoE metrics, operators, today, face remarkable difficulty in assessing the performance of EV Charging Stations (EVCSs) in this regard. This paper aims at filling this gap through the formulation of novel and original critical QoE performance metrics that provide operators with visibility into the per-EVCS operational dynamics and allow for the optimization of these stations' respective utilization. Such metrics shall then be used as inputs to a Machine Learning model finely tailored and trained using recent real-world data sets for the purpose of forecasting future long-term EVCS loads. This will, in turn, allow for making informed optimal EV charging infrastructure expansions that will be capable of reliably coping with the rising EV charging demands and maintaining acceptable QoE levels. The model's accuracy has been tested and extensive simulations are conducted to evaluate the achieved performance in terms of the above listed metrics and show the suitability of the recommended infrastructure expansions.

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