Electric Vehicle Charging Data Analytics of Corporate Fleets

Abstract

The advances in electric mobility, motivated by current sustainability issues, have led public and private organizations to invest in the electrification of their corporate fleets. To succeed in this transition, companies must mitigate the impacts of electrification on their fleet operation, in particular the ones on vehicle recharging. The increase in energy demand caused by electrification may require changes in the company electrical infrastructure, the installation of charging stations, and the proper planning of the recharging schedule, considering the particularities of each fleet and operation. In this context, data analytics is seen as an important tool to help companies to understand their charging fleet profile, supporting decision makers in making data-driven decisions regarding their charging infrastructure. This paper shows how data analytics could be applied to analyze the charging data of corporate electric fleets, adopting a business-oriented analysis method based on the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. The analyses were performed on data collected from three different companies, with each one of them operating fleets of vehicles of different categories, i.e., ultra-light, light, and heavy vehicles. The results illustrate how data analytics, based on interactive reports and dashboards, can shed light on business questions related to the operation of electric vehicle corporate fleets.