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With the electrification in freight transportation, the availability offast-charging facilities becomes essential to facilitate en-route charging forfreight electric vehicles. Most studies focus on planning charging facilitiesbased on mathematical modeling and hypothetical scenarios. This study aims todevelop a data-driven integrated framework for fast-charging facility planning.By leveraging the highway traffic data, we extracted, analyzed, and comparedspatial and temporal flow patterns of general traffic and freight traffic.Furthermore, graph theory-based network evaluation methods are employed toidentify traffic nodes within the highway network that play a significant rolein accommodating charging infrastructure. A candidate selection method isproposed to obtain potential deployment locations for charging stations andto-go chargers. Based on this, we present a multi-period bi-objectiveoptimization model to provide optimal solutions for the placement of chargingfacilities, with the objectives of minimizing investment cost and maximizingdemand coverage. The case study on the Amsterdam highway network shows howexisting traffic data can be used to generate more realistic charging demandscenarios and how it can be integrated and evaluated within the optimizationframework for facility planning. The study also shows that the proposed modelcan leverage the potential of early investment in improving the charging demandcoverage.

A Data-Driven Integrated Framework for Fast-Charging Facility Planning using Multi-Period Bi-Objective Optimization