Optimization of future charging infrastructure for commercial electric vehicles using a multi-objective genetic algorithm and real travel data

Abstract

Electric mobility has gained much interest in the automotive industry and among commercial customers. A well-developed charging infrastructure is a fundamental requirement to meet the rising demand for electricity. The aim of this contribution is to demonstrate how optimization can be used for the extension of public charging infrastructure for electric vehicles (EVs). The suitability of conversion from combustion engines to EVs for commercial customers is evaluated for different scenarios. The impact of an expanded charging infrastructure is measured by a multi-objective genetic algorithm. The location and type of charging stations is optimized with respect to the number of failed trips, due to empty batteries, and the total cost of infrastructure. Assuming that the usage of commercial vehicles ist unaltered when switching to EVs, travel data from commercial vehicles with combustion engines may serve as a starting point for the optimization of the charging infrastructure. The resulting pareto front may support decision makers in placing optimal public charging stations.