Connected E-Mobility, IoT and its Emerging Requirements for Planning and Infrastructures

Exner, J. P., Bauer, S., Novikova, K., Ludwig, J., & Werth, D. (2020)
In: SHAPING URBAN CHANGE–Livable City Regions for the 21st Century. Proceedings of REAL CORP 2020, 25th International Conference on Urban Development, Regional Planning and Information Society, CORP–Competence Center of Urban and Regional Planning, S. 175-181

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The worldwide expansion of electric vehicle usage comes with the challenge of adapting the respective traffic and charging station infrastructure in cities, resulting in profound changes not only to traffic itself but also to the way energy is being distributed. An ongoing all-embracing real-time IoT-network will provide a vast amount of new possible data for planners, for example in order to get useful insights into the relationship between traffic flow patterns and loading patterns of e-vehicles. For this, the use of e-mobility data is essential. This paper will present potential ways of gathering data for infrastructure planning and user- oriented recommendations, such as movement patterns and charging status of vehicles, since this information is already available but not being shared by car companies. The given perspective will take into consideration individual vehicles and vehicle fleets as well as the traffic network as a whole. Efficient access to current and predicted load for charging stations in the electric vehicle transition would be a beneficial factor for the promotion of electricity-powered vehicles. In this paper, we lay out different approaches. Besides manufacturer related data-acquissition, this includes as well to provide this information via user- generated content (UGC), and to derive charging recommendations from a user-oriented, intelligent recommendation system. Furthermore, the challenges regarding the effects towards the electricity network and the necessity of physical charging infrastructure will be discussed. In a future situation of more heterogenous and dispersed energy supply, being able to predict the energy consumption of e-vehicles individually and instantaneously based on collected data will be a necessity for energy supplying companies. This will intentionally lead to more grid stability, by distributing electricity inside the network based on predicted loads, as well as work as an incentive for drivers to make the transition to e-mobility, because finding e-charging stations will eventually not be an issue. This paper will take the respective questions and potential control mechanisms for traffic patterns into consideration.