The electrification of commercial vehicle fleets presents a critical opportunity for decarbonizing urban logistics. However, it introduces complex challenges, particularly in balancing infrastructure investments, energy system integration, and operational cost control. This study addresses these challenges by developing a strategic decision-support model aimed at optimizing long-term infrastructure planning for electric delivery fleets. Special emphasis is placed on the role of bidirectional charging and the integration of local renewable energy sources such as photovoltaic (PV) systems.

The core of the work is a mathematical optimization model formulated as a Mixed-Integer Linear Programming (MILP) problem. It evaluates infrastructure and operational configurations based on real-world input data, including dynamic electricity tariffs, high-resolution hourly PV generation profiles, vehicle energy consumption, and operational schedules from an ongoing electrification project in Switzerland. The case study involves a parcel logistics company aiming to fully electrify its delivery fleet across 12 national hubs by 2035. The analysed site, a former freight yard now undergoing redevelopment, provides a unique opportunity to explore infrastructure decisions within a constrained urban context.

The model minimizes the Total Cost of Ownership by applying two complementary analyses to each scenario: a conventional assessment of infrastructure and vehicle investment costs, and a dynamic evaluation that captures time-dependent operational parameters. These include vehicle usage and charging schedules, photovoltaic generation patterns, and temporal variations in electricity prices. The model simultaneously optimizes the sizing of key infrastructure elements, such as photovoltaic systems, stationary battery storage, vehicle batteries, and the grid connection, while accounting for realistic technical and temporal constraints. In addition, it determines cost-efficient charging and discharging strategies, incorporating bidirectional energy flows and dynamic tariff structures. Embedded in a scenario-based framework, the model enables systematic exploration of infrastructure configurations to identify cost-effective solutions and enhance overall system resilience.

Key findings show that temporal alignment between PV production and fleet availability significantly affects cost outcomes. Bidirectional charging proves especially beneficial for maximizing self-consumption of PV electricity and reducing dependency on expensive grid imports during peak hours. Seasonal planning and the strategic sizing of battery storage also play crucial roles in minimizing operational expenses. The results provide actionable insights for logistics operators, energy planners, and policymakers aiming to design cost-effective, resilient, and scalable fleet electrification strategies.

The research is based entirely on original modelling and analysis, supported by field data collected in collaboration with project partners, including a logistics provider, energy supplier, and charging infrastructure manufacturer. It is part of an applied research project and is situated within the context of a larger academic and industrial collaboration.