The successful introduction of electromobility relies considerably on the implementation of the charging stations. This implementation, should be cost effective and satisfy the energy demand of the electric vehicle users. This article presents a tool that computes the optimal charging infrastructure, by considering the placement and type of charging stations. To achieve this, we first calculate the spatiotemporal energy demand to account for the specific demand of each user. Next, we conduct a preselection step, where the locations and station types of little relevance are identified and excluded from optimization. The actual optimization step uses a multi-objective genetic algorithm with two objectives: minimizing the total installation costs of the infrastructure and minimizing the amount of trips that fail due to insufficient energy in the vehicles. Finally, the study analyzes two factors, which possibly influence the optimization algorithm: the user's charging behavior and developments of the battery energy efficiency.     «

The successful introduction of electromobility relies considerably on the implementation of the charging stations. This implementation, should be cost effective and satisfy the energy demand of the electric vehicle users. This article presents a tool that computes the optimal charging infrastructure, by considering the placement and type of charging stations. To achieve this, we first calculate the spatiotemporal energy demand to account for the specific demand of each user. Next, we conduct a p...     »