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.

翻译：本文提出了一套基于真实数据的调研与评估框架，旨在为运营商深入洞察公共电动汽车充电基础设施中消费者感知的服务体验（QoE）提供支撑。受电动汽车市场空前增长的驱动，现有充电基础设施预计将难以持续满足快速增长的充电需求；更遑论当前采用的临时性基础设施扩展策略，似乎远未能提供切实的优质服务可持续性方案，以切实减少（乃至最终缓解）这一问题的严重性。由于缺乏合适的QoE度量指标，当前运营商在评估电动汽车充电站（EVCS）相关性能时面临显著困难。本文旨在通过构建新颖且原创的关键QoE性能指标来填补这一空白，这些指标可使运营商能够洞察每个EVCS的运行动态，并优化各站点的利用率。随后，这些指标将被用作机器学习模型的输入，该模型利用近期真实数据集进行精细定制与训练，以预测未来长期EVCS负载。这将进而支撑做出信息充分的最优充电基础设施扩展决策，使其能够可靠应对日益增长的充电需求，并维持可接受的QoE水平。模型精度已通过测试，并开展了广泛仿真以评估上述指标所达成的性能，同时展示了推荐的基础设施扩展方案的适用性。