Discovering EV Recharging Patterns through an Automated Analytical Workflow

Abstract

The vision for smart cities is to provide a core infrastructure that enables a good quality of life for their citizens and the sustainable management of natural resources. Towards this vision, supporting the adoption of Electric Vehicles (EV) contributes to improved air quality, sustainable mobility, and utility distribution. Fostering EV adoption contends with concerns typically centered on vehicle range and costs. An understanding of EV charging patterns is therefore crucial for optimizing charging infrastructure placement and managing operational costs. Towards this end, this paper proposes an automated analytical workflow to gain insight from a large volume of real operational data from EV charging stations. The research goal is to establish a mechanism to descriptively analyse the EV charging data and to thoroughly diagnose whether low-demand charging station groupings can effectively be identified using spatio-temporal features and hierarchical clustering. Preliminary results suggest agglomerative clustering is effective at grouping similar charging stations together when considering spatial and temporal features of recharge events.