Who Can Drive Electric? Segment-Specific Assessment of Reachability and Grid impact of EVs and PHEVs

Research Highlights

- Natural driving data of 1,000 vehicles serves as input for a range and grid impact assessment of EVs

- Range requirements and energy demand varies substantially between driver segments

- Impact and feasibility studies should be segment-specific to yield meaningful results



The mobility behavior of individuals largely determines the practical range of electric vehicles, the resulting grid impact, and the requirements regarding battery capacities and charging infrastructures. Yet, current assessments assume average “one-fits-all” mobility profiles and derive estimates that disregard variations in driving behaviors, trip characteristics, and parking behaviors.



Variables including mileage, speed as well as parking durations and parking locations are extracted from GPS data of more than 1,000 conventional vehicles for over one year. A partitioning-based clustering approach is set up to derive distinct segments of drivers. The findings enable a segment-specific analysis that explains energy demand and practical driving range of different electric vehicles for each group of drivers.   



The segmentation approach reveals that there are substantial differences in the mobility behavior of individual drivers that lead to large differences in their expected electric mobility and charging demand. The share of mileage that can be driven electrically, for example, can vary by 45 percent points between different segments (assuming plug-in hybrid electric vehicles with an 18.8 kWh battery and charging opportunities limited to the home location). The study suggests that a driver population should not be considered as a coherent whole but that distinct groups should be considered individually in order to improve the validity of usability and impact assessments.

Selected publications

Wenig, J., Sodenkamp, M., Staake, T. (2015) Data-based Assessment of Plug-in Electric Vehicle Driving. Lecture Notes in Computer Science (9424), pp. 115-126.



The research has been funded in part by the Technology Alliance of Upper Franconia (TAO), Germany.

Date: 2014 - 2018



Jürgen Wenig, Thorsten Staake, Frédéric Thiesse, Mariya Sodenkamp

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