One of the current challenges in transport is e-mobility, understood as electromobility, ecomobility and mobility economics. E-mobility is nowadays a key focus area of socio-technical change, in terms of attention from policy makers as well as from industry and the public. The societal challenges ahead, in particular climate change put increasing pressure on the current mobility system, due to its tremendous environmental impact. Furthermore e-mobility is seen as major economic opportunity for the automotive industry. In this context, based on the available literature and documents and practical solutions implemented already in cities and agglomerations, the paper discusses the fundamental challenges that cities are to face in the context of the current megatrends. This process will engage diff erent stakeholders in order to make this concept become a reality.

Transformer efficiency and regulation, are to be maintained at maximum and minimum respectively by optimal loading, control, and compensation. Charging of electric vehicles at random charging stations will result in uncertain loading on the distribution transformer. The efficiency reduces and regulation increases as a consequence of this loading. In this work, a novel optimization strategy is proposed to map electric vehicles to a charging station, that is optimal with respect to the physical distance, traveling time, charging cost, the effect on transformer efficiency and regulation. Consumer and utility factors are considered for mapping electric vehicles to charging stations. An Internet of Things platform is used to fetch the dynamic location of electric vehicles. The dynamic locations are fed to a binary optimization problem to find an optimal routing table that maps electric vehicles to a charging station. A comparative study is carried out, with and without optimization, to validate the proposed methodology.