Electric vehicle fast charging infrastructure planning in urban networks considering daily travel and charging behavior

Electric vehicles are a sustainable substitution to conventional vehicles. This study introduces an integrated framework for urban fast charging infrastructure to address the range anxiety issue. A mesoscopic simulation tool is developed to generate trip trajectories, and simulate charging behavior based on various trip attributes. The resulting charging demand is the key input to a mixed-integer nonlinear program that seeks charging station configuration. The model minimizes the total system cost including charging station and charger installation costs, and charging, queuing, and detouring delays. The problem is solved using a decomposition technique incorporating a commercial solver for small networks, and a heuristic algorithm for large-scale networks, in addition to the Golden Section method. The solution quality and significant superiority in the computational efficiency of the decomposition approach are confirmed in comparison with the implicit enumeration approach. Furthermore, the required infrastructure to support urban trips is explored for future market shares and technologies.

Automated summary

This study introduces an integrated framework for urban fast charging infrastructure to address the range anxiety issue. The model minimizes the total system cost including charging station and charger installation costs, and charging, queuing, and detouring delays. The solution quality and significant superiority in computational efficiency of the decomposition approach are confirmed in comparison with the implicit enumeration approach.