A Data-Driven Framework for Improving Public EV Charging Infrastructure:
  Modeling and Forecasting
Nassr Al-Dahabreh Mohammad Ali Sayed Khaled Sarieddine Mohamed Elhattab Maurice Khabbaz Ribal Atallah Chadi Assi
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.