Usage Profiling in Electric Vehicles

Abstract

In the overall effort of reducing CO2 emissions, the significance of alternative drive engines is growing. The transition from combustion engine vehicles to electric vehicles is high on the political agendas, with governments providing extensive funding to promote electric mobility. However, there are still challenges that hamper the dissemination of electric vehicles. One of those challenges is the limited range and the resulting range anxiety. Displayed vehicle range data contribute to this, as they are relatively inaccurate and might vary quite strongly during individual trips. This problem could be addressed by personalizing the range display according to the driving style of the current driver. Driver assistance services, like distance control, are becoming increasingly personalized nowadays, however, they are predominantly designed for internal combustion engine vehicles. In this paper, relevant input parameters for classifying the driving styles of electric vehicle users are identified. Furthermore, a system based on real-life driving data is developed to determine the driving style. Real-life driving data were collected in experiments and used to profile the driving style by means of fuzzy logic. Based on the results, an approach for a realistic classification of driving styles of electric vehicle users is discussed.