The development of hydrogen refueling infrastructure plays a strategic role in enabling the decarbonization of the transport sector, especially along major freight and passenger corridors such as the Trans-European Transport Network (TEN-T). Despite the growing interest in hydrogen mobility, existing methodologies for the optimal location of hydrogen refueling stations (HRS) remain fragmented and often overlook operational dynamics. Following a review of the existing literature on HRS location models and approaches, this study highlights key methodological gaps that hinder effective infrastructure planning. In response, a two-stage framework is proposed, combining a flow-based location model with a stochastic queueing approach to determine both the optimal placement of HRS and the number of dispensers required at each site. The method is applied to a real segment of the TEN-T network in Northern Italy. The results demonstrate the flexibility of the model in accommodating different hydrogen vehicle penetration scenarios and its utility as a decision-support tool for public authorities and infrastructure planners.

This article investigates the feasibility of hydrogen-based retrofitting solutions for light commercial vehicles operating in urban freight transport. The analysis is based on a mission-driven methodology applied to a representative urban case study in the city of Rome, using synthetic route profiles and vehicle specifications derived from manufacturer datasheets. Three representative urban delivery missions are defined, characterised by cumulative daily distances of approximately 190–200 km and associated energy requirements in the range of 54–57 kWh. These mission profiles are first used to assess a commercially representative battery electric vehicle configuration, for which the usable onboard battery energy is estimated at 41.6 kWh. The results show that, under the considered operating conditions, the battery electric configuration is not able to complete the planned routes without intermediate recharging. On this basis, a fuel cell hybrid electric vehicle retrofit configuration is evaluated, combining a 35 kWh battery, a 45 kW fuel cell system and 3.5 kg of onboard hydrogen storage at 350 bar. The resulting estimated driving range is approximately 293 km, which is sufficient to satisfy the defined mission requirements. This study is framed as a technical feasibility assessment and does not aim to provide optimisation or experimental validation. The proposed methodology can be applied to other urban contexts by adapting route characteristics and daily mileage requirements.

The integration of electric vehicles into urban energy systems offers opportunities for vehicle-to-grid (V2G) flexibility, but estimating the spatial and temporal availability of battery energy requires a detailed understanding of vehicle mobility and parking patterns. This study presents a mobility-data-driven assessment of potential V2G energy availability across three urban zones, using Floating Car Data collected over five weekdays. Building on a previously established mobility-based electrification framework, the analysis reconstructs vehicle parking intervals and estimates the battery energy potentially available during each stop, accounting for energy consumption associated with preceding and subsequent trips. An energy-availability constraint is introduced to preserve a minimum battery state of charge of 30% after subsequent travel. The resulting 15-min profiles are compared across days and zones to examine temporal variability and sensitivity to the energy-availability assumptions. In the analyzed sample, the maximum simultaneous energy availability under the original formulation ranges from approximately 6.08 to 6.45 MWh across the five days. Enforcing the final-reserve constraint reduces these daily maxima by approximately 11.8–14.0%. These estimates represent conditional energy-availability potentials rather than measured grid exports or forecasts of V2G power delivery. The findings illustrate how mobility observations can support the characterization of urban V2G energy resources while highlighting the importance of explicit battery-reserve assumptions and the limitations imposed by the absence of charging-infrastructure and connection data.