An open tool for creating battery-electric vehicle time series from empirical data, emobpy

There is substantial research interest in how future fleets of battery-electric vehicles will interact with the power sector. Various types of energy models are used for respective analyses. They depend on meaningful input parameters, in particular time series of vehicle mobility, driving electricity consumption, grid availability, or grid electricity demand. As the availability of such data is highly limited, we introduce the open-source tool emobpy. Based on mobility statistics, physical properties of battery-electric vehicles, and other customizable assumptions, it derives time series data that can readily be used in a wide range of model applications. For an illustration, we create and characterize 200 vehicle profiles for Germany. Depending on the hour of the day, a fleet of one million vehicles has a median grid availability between 5 and 7 gigawatts, as vehicles are parking most of the time. Four exemplary grid electricity demand time series illustrate the smoothing effect of balanced charging strategies.

Automated summary

This paper explores the interaction between future fleets of battery-electric vehicles and the power sector, introducing the open-source tool emobpy for generating time series data that can be applied in various models. By analyzing physical properties and mobility statistics, 200 vehicle profiles for Germany were created and characterized, with a demonstration of the smoothing effect of balanced charging strategies on grid electricity demand.